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Rodriguez-Esteban, Raul. Methods in biomedical text mining. Ph.D. Thesis, Columbia University, 2008.

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Methods in biomedical text mining
Raul Rodriguez-Esteban
Columbia University, December 2007


1  Overview of text mining of biomedical interactions
1.1  Text mining
1.2  Biomedical text mining
1.3  Interactions from text
1.3.1  GENIES
1.3.2  GeneWays
1.4  Curation and evaluation
2  Automatic curation of text-mined facts
2.1  Introduction
2.2  Approach
2.3  Methods
2.3.1  Training data
2.4  Mathematical background
2.4.1  Machine-learning algorithms
2.4.2  Features used in our analysis
2.4.3  Separating data into training and testing: Cross-validation
2.4.4  Comparison of methods: Receiver operating characteristic (ROC) scores
2.5  Results
2.6  Discussion
3  Overview of biomedical term recognition and classification
3.1  Introduction
3.1.1  Term recognition
3.1.2  Named entity expressions
3.1.3  Term classification
3.1.4  Biomedical term recognition and classification
3.2  Mathematical Background
3.2.1  Recognition and classification framework
3.2.2  Conditional random fields
4  Biomedical term recognition and classification using large corpora and search engines
4.1  Introduction
4.2  Term recognition
4.2.1  Text pre-processing and indexing
4.2.2  Syntactical model
4.2.3  Term recognition process
4.3  Term classification
4.3.1  Features
4.3.2  Local, regional, global: Word sense disambiguation
5  Six senses: the bleak sensory landscape of biomedical texts
6  A recipe for high impact
6.1  Ingredients of a scholarly study
6.2  Information flow through publication-type niches
6.3  Additional information
6.3.1  Data
6.3.2  Analysis
7  How many scientific papers should be retracted?
7.1  Analyzing retraction patterns
7.2  Mathematical model to calculate the number of articles that should have been retracted
7.3  Retraction rates are on the rise
8  Future work and conclusions
8.1  Future work
8.2  Conclusion
8.3  Papers that resulted from the work in this thesis
9  Bibliography

Chapter 1
Overview of text mining of biomedical interactions

The purpose of this introduction is to give a historical overview and background to the project of automatic curation of text-mined data described in Chapter 2. This introduction describes the beginnings of text mining as a discipline and its arrival to the biomedical domain. It also describes the first interaction text mining projects, which precede and set a path to the development of GeneWays. This introduction is necessary to understand the architectural and structural choices made for the design of GeneWays. Finally, this introduction reviews the different efforts made in evaluating text-mined interaction data before the automatic curation project was developed. The aim is not only to contextualize the state of the art and decisions made during the project but also to present the basis on which it stood and the challenges it faced.

1.1  Text mining

The field of text mining is a relatively new discipline born of the knowledge discovery in databases (KDD) and data mining (DM) community. As it is often the case when a discipline is born, it borrowed techniques and approaches from similar, more established fields before establishing its own identity 1.

Alessandro Zanasi claims that the first time he heard the term “text mining” was when it was spoken by Charles Huot in 1994, during the IBM-ECAM (European Centre for Applied Mathematics) [1] workshop in Paris. Whether it was used with the same meaning that it has today, in the context of applications such as information extraction [2] or document classification [3], is unclear. In 1995 and 1996, Ronen Feldman and colleagues offered the first contributions to the field that can be called text mining with more certainty [4,5,6], originally called knowledge discovery in text (KDT). The word mining was soon introduced in 1996 in the context of KDT, followed by the coinage of the name “text mining”, a variation of the name data mining. By 1997 the expression text mining had become an accepted name for the new discipline. The new discipline quickly spawned courses, workshops, and books and opened new avenues of research and notable subfields, such as web text mining (1998) and biomedical text mining (1998). Text mining brought together researchers from the KDD and DM communities and from the fields of natural language processing (NLP), automatic knowledge acquisition, information retrieval, and information extraction, to name a few. Text mining became the predominant name for the discipline, widely replacing other names such as KDT, KDT and text mining, textual data mining, and text data mining. That some of these names are still in use reflects not only a stylistic choice but also, in some cases, differences in understanding of aims, scope, and methods.

Marti Hearst [7] was one of the first to summarize the state of the nascent discipline in 1999, attempting to define its scope with respect to other fields such as data mining, computational linguistics, or information retrieval. Hearst stressed that the defining quality of text mining is that its goal is to discover novel information, unlike fields such as information retrieval and data mining. In this respect, text mining is indebted to literature-based discovery, a field championed by Don Swanson beginning with his seminal paper in 1986, “Undiscovered public knowledge” [8,9]. Literature-based discovery was intended to be a systematic search for pieces of knowledge that could be combined to create a novel discovery. Originally, literature-based discovery was largely a non-automated process. Swanson recalled stumbling onto the idea through a serendipitous finding of two unrelated articles that could be combined to answer a question that no other single article answered. His acceptance speech upon receiving the American Society for Information Science and Technology (ASIST) 2000 Award of Merit is worth quoting because it addresses core principles of the text-mining field:

“More than 40 years ago the fragmentation of scientific knowledge was a problem actively discussed but without much visible progress toward a solution; perhaps people then had the consummate wisdom to know that no problem is so big that you can’t run away from it. Three aspects of the context and nature of this fragmentation seem notable:

1. The disparity between the total quantity of recorded knowledge, however it might be measured, and the limited human capacity to assimilate it, is not only enormous now but grows unremittingly. Exactly how the limitations of the human intellect and life span affect the growth of knowledge is unknown. Metaphorically, how can the frontiers of science be pushed forward if, someday, it will take a lifetime just to reach them? […]

2. In response to the information explosion, specialties are somehow spontaneously created, then grow too large and split further into subspecialties without even a declaration of independence. One unintended result is the fragmentation of knowledge owing to inadequate cross-specialty communication. And as knowledge continues to grow, fragmentation will inevitably get worse because it is driven by the human imperative to escape inundation.

3. Of particular interest to me is the possibility that information in one specialty might be of value in another without anyone becoming aware of the fact. Specialized literatures, or other “units” of knowledge, that do not intercommunicate by citing one another may nonetheless have many implicit textual interconnections based on meaning. Indeed the number of unintended or implicit text-based connections within the literature of science may greatly exceed the number that are explicit, because there are far more possible combinations of units (that potentially could be related) than there are units. The connection explosion may be more portentous than the information explosion.”

Heart’s opinion is shared by Ananiadou and McNaught [10] and others [11]: “The primary goal of text mining is to retrieve knowledge that is hidden in text, and to present the distilled knowledge to users in a concise form”. However, a more common point of view, first proposed by Ronen Feldman, defines text mining as different from data mining only because it deals with data that by its nature is unstructured, unlike data organized in databases, which are the primary source for data mining [4,12,13,14,15]. Kao and Poteet [16] go even further, stating that “Text mining is the discovery and extraction of interesting, non-trivial knowledge from free or unstructured text. This encompasses everything from information retrieval (i.e., document or web site retrieval) to text classification and clustering, to (somewhat more recently) entity, relation, and event extraction.” In practice, this expansive view of text mining is not shared by many others, especially considering that information retrieval or text classification predate text mining by many years. Kao and Poteet’s opinion implies that text mining is an umbrella term covering a laundry list of textual processing methods. A more common view seems to be that the aim of text mining is to find interesting, useful, or valuable patterns-that are not necessary novel-in text collections. This perspective places text mining closer to knowledge acquisition and information extraction.

Given the fuzzy lines that separate text mining from similar fields, it is not clear whether it can be defined meaningfully beyond a mix of different conceptions held by different researchers. The confusion is compounded further because applications from related fields may be regarded as necessary processing steps for effective text mining. In other words, text-mining projects might require sub-tasks from other fields. Therefore, text mining in some contexts might be used for the sole purpose of indicating the scientific agenda in which the study should be considered, not for defining the task itself as “text mining”. Furthermore, as other fields have built on advances in text mining, text mining also has become an intermediate step in projects of different nature.

Related disciplines such as semantic analysis, text analysis, information retrieval, information extraction, and knowledge acquisition have a much older pedigree within the computation and information sciences than does text mining. Like text mining, they derive from activities that originally could be handled by human intellect and rudimentary record-keeping but became more complex with the progressive accumulation of knowledge and information. Fielden [17] plotted the evolution of the size of information repositories over the course of human history, showing an exponential growth in the last decades. More comprehensively, Peter Lyman and Hal Varian led a study designed to estimate the quantity of information produced worldwide every year [18,19]; they estimated a grand total of 5 exabytes2 , or 800 megabytes per person per year, of which 92% were in magnetic storage. Printed text represented 33 terabytes, whereas the “surface internet” accounted for 167 terabytes and the “deep internet” (or database, dynamically-generated pages) for about 92 petabytes). This unparalleled growth has been accompanied by extraordinary improvements in the devices and methods in the different computation and information sciences. Text mining, a late arrival, has the advantage of drawing from an extensive set of diverse techniques developed not only in the related disciplines, but also in other fields such as machine learning, artificial intelligence, probabilistic analysis, statistics, pattern recognition, data management, and information theory. While other disciplines, like information retrieval, fledged out before the current pervasive use and availability of electronic text, text mining was born in a seemingly limitless and growing frontier of resources and opportunities. Text miners, in turn, have acted like they have a hammer and see a nail in everything. Perhaps this is the best explanation for the success of text mining: Applications have driven its evolution [1].

Given the fragmentary state of the field, it is not surprising that there is not currently a journal that specializes in text mining. The door is open for further transformation of the text-mining domain, whether in terms of its buzz or its consolidation in the spectrum of computation and information sciences.

1.2  Biomedical text mining

The first attempts at text mining of the biomedical literature date back to 1998. As explained in Section 1.1, the label “text mining” may have consumed some areas that formerly went by a different name, such as knowledge acquisition and information extraction. Text mining builds on previous informatics and computational work on semantic analysis, dictionary creation, knowledge acquisition, classification, etc. Its application to the biomedical realm is a natural extension given the existing opportunities: exponential growth of the literature-both in size and in electronic availability-; the gradual shift to electronic medical records; the on-going work in annotated resources (e.g., Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), Swissprot); and the increasing need for integration between information sources of disparate origin, also known as integromics [20]. The internet is the main engine that has fuelled this growth. Even though computers and electronic communications long predated the internet, it is the internet that has crystallized change because it has dramatically lowered the cost of information access and exchange and brought to the social forefront the challenges and opportunities of biomedical electronic information (e.g., the Health Insurance Portability and Accountability Act of 1996; the Open Access movement).

Integromics is proving to be of crucial importance in current developments as more data are becoming available in different formats in electronic and on-line form, including supplementary information tables, genome linkage maps, DNA sequences, taxonomies, ontologies, hospital medical records, and semi-structured forms (e.g., questionnaires), etc. An example is Medline [21], an exponentially growing biomedical bibliography that accounts for upwards of 16 million articles. In many cases, Medline has references to full-text articles that may be retrieved with the appropriate licenses. However, information related to those articles, like on-line repositories or supplementary text and tables, is harder to access. Medline’s growth can be considered even more dramatic if we include the “deep Medline” trove of additional resources that are ready for mining.

Biomedical text miners may claim the superiority of text-mined data over other resources, especially over manually curated data. Text mining casts a wide net over the biomedical spectrum, allowing individual researchers to deal with Swanson’s three arguments for library knowledge discovery (see Section 1.1). The resulting catch is larger than can typically be gathered manually. As of October 2007, the hand-curated Database of Interacting Proteins (DIP, [22]) held 56,186 interactions. Perhaps the most extensive effort in manual literature-derived extraction of interactions is BioGRID [23], with 70,000 interactions. In comparison, some text-mining interaction repositories hold over half a million interactions (see Section 1.3.2). The NCBI Gene Expression Omnibus repository of microarray expression datasets contains about half a billion data samples [24]. Hence, text-mined biomedical data has a place within the suite of tools available to biomedical informaticians and researchers. For the examples given, this place lies somewhere between high throughput methods and manually hand-curated sets, each with its own niche applications. The challenge for biomedical text mining is to assert its usefulness both for acquiring information with quality that approaches (or surpasses) hand-curated data and for reaching the widest coverage for system-wide analysis (e.g., characterizing complex diseases [25]).

Some applications in biomedical text mining have mirrored those of text mining at large, like document classification, data integration, literature-based discovery, and literature analysis (e.g., scientific trends and emerging topics [26]). Others have been more specific to biomedicine, such as biomedical annotation, phenome/phenotype analysis, public health informatics (e.g., news analysis [27], hospital rankings [28]), clinical informatics, and nursing informatics. The most flourishing areas, however, may be loosely defined as those closely linked to systems biology [29,30] and medical text mining. The former deals with such topics as biomedical interaction extraction, functional analysis, or genome annotation among others (see a list of main tools and repositories in [31]). The latter deals with the range of narratives found in the textual supports associated with clinical settings, from the ICU bedside to the clinical trials desk. Biomedical text-mining articles are published mostly in journals and conferences in biomedical informatics and computational biology, and sometimes in non-informatics journals like Genome Biology.

1.3  Interactions from text

Systems biology has been a hotbed for developments in biomedical text mining, as mentioned in Section 1.2. One of the focuses has been on interactions between different types of molecules, especially proteins (i.e., PPI, protein-protein interactions). The success of PPI can be seen in its application for integrative studies, the popularity of its tools, and its use as support for public databases like DIP [32], MINT [33], and BIND [34]. The interactions, taken from a functional genomics point of view, range from physical (e.g. protein binding) to indirect (e.g., proteins in the same pathway but not physically interacting) interactions to other phenomena, such as co-expression. The interaction triplet has been an important text-mining interaction model since its inception. This triplet consists of the two elements that are involved in the interaction and the verb or action word that relates them. In the GeneWays ontology [35], the elements of the triplet are called the upstream term, downstream term, and action. Triplets usually are taken from single sentences. For example, in the sentence “Gene A activates gene B.”, “gene A” is the upstream term, “gene B” is the downstream term, and “activate” is the action. This model was first introduced in a preliminary study by Sekimizu and colleagues [36], in which they sought verbs that could characterize gene-gene interactions. Rindflesch and colleagues [37] experimented with sentences that included the verb “bind”.

An alternative model to triplets is used in co-occurrence studies. Stapley and Benoit [38], for example, used co-occurrence in a study of selected PubMed abstracts, followed later by the larger-scale project PubGene [39]. With this method, interactions are inferred from co-occurrence statistics of two terms in documents. If the terms co-occur in text more often than could be expected, it is argued, that there is basis to suggest that they may be related. Co-occurrence is a statistical method used early in information retrieval. It has been used in different types of analyses but, due to its limitations, it has not become a method of general use in the text mining of interactions. Co-occurrence, for example, is of limited help in distinguishing interactions of very low frequency. Another drawback of this method is that the nature of the interaction is lost.

Some of the problems that biomedical interaction extraction entails are common to biomedical texts at large, such as extensive and open-ended vocabulary, erratic abbreviations, word sense ambiguity, and convoluted sentences. Others are more specific. For example, negative particles or words with negative meaning may completely change the meaning of an interaction, e.g., “We could not find any interaction between gene A and gene B” (for a study on a negative interactome, see [40]). Anaphora is another challenging problem that rarely is tackled (although see [41]). Anaphora refers to situations in which the name of an object is elided, generally because a pronoun is used to avoid repetition, e.g. “It activates gene B.” The challenge is to identify the object to which “it” refers. More generally speaking, biomedical interaction extraction faces the hurdles of the different pre-processing steps plus the complexity of identifying the interactions themselves.

Blaschke and colleagues [42] proposed an early rule-based model for interaction extraction that tried to capture a simple lexical pattern in sentences: “protein A – action – protein B”. The names of the proteins and the action were identified using controlled vocabularies. Thomas and colleagues [43] used syntactic instead of lexical patterns (an example of a syntactic pattern is noun phrase – verb – noun phrase) to find triplet candidates that were then narrowed down through a hand-crafted scoring system. The syntactic analysis performed was of the shallow type, which can be done more quickly than deep or full parsing and it only identifies units at the syntagma level of the sentence (e.g., noun phrases and verb phrases). Proux and colleagues [44] developed the approaches used in [43] and [42] by using first syntactic parsing and then applying lexical patterns to find interactions. Similar approaches were explored by [45] and [46], although they did not report to have fully implemented them. Blaschke and colleagues created a generalized pattern approach, calling these patterns “frames” [47,48]. Frames are flexible patterns that may include additional information to enrich the analysis (e.g., the distance in words between the interaction terms of the sentence). Park and colleagues [49] and Yakushiji and colleagues [50] went further by including full syntactic parsing in their systems. Full parsing allows for categorization of all syntactic dependencies among the words of a sentence. The GENIES parser [51] was born within this context of incipient improvement and tests of new approaches.

1.3.1  GENIES

GENIES [51] evolved from MedLee [52], a medical natural-language processing application in use at the Clinical Information System (CIS) of New York Presbyterian Hospital. MedLee was inspired by the sub-language theory of Zellig Harris [53], its main trait was its semantic grammar, which combined grammatical patterns and lexical information to capture different structures. In contrast to other semantic grammar systems, pattern matching in MedLee must be exact. Only the sequences that conform precisely to a specific pattern both grammatically and semantically are considered. This approach was chosen to extract information both efficiently and reliably.

The MedLee processing pipeline consists of several parts:

  • Pre-processor: The text is formatted for manipulation and then analyzed lexically using a lexicon.
  • Parser: The text is analyzed grammatically (deep grammatical parsing). If the grammatical parsing fails there is an error recovery step to break the sentences into segments more amenable to manipulation. These segments then are analyzed grammatically.
  • Phrase recognition: Terms that are adjacent and that could form a phrase are combined together (e.g., “chest” followed by “pain” is combined into “chest pain”).
  • Encoder: Terms are mapped to a controlled vocabulary.

GENIES uses the pre-processor and parser modules from MedLee adapting them to the systems biology domain. The semantic categories in GENIES (e.g., amino acid, cell, complex, domain, DNA region, etc.) mostly differ from those in MedLee (e.g., body location, finding, device, disease, procedure, etc.) although a few overlap (e.g., certainty, connective). The grammar rules of the parser module were adapted to the new semantic categories. An important difference from Harris’s sub-language theory is that the patterns were constructed manually from perceived patterns of interest in biomedical texts, whereas Harris proposed using statistical methods. Furthermore, GENIES receives its input from a term tagger that uses BLAST [54] pattern-matching algorithm to recognize a term even if it is written with slight variations [55].

1.3.2  GeneWays

GeneWays [35] is a system designed for the automatic extraction of signal transduction pathways, although, more broadly, it can be characterized as a system designed to capture gene, protein, and small molecule interactions. GeneWays 6.0 stores 4,035,759 relationships (of which 2,652,916 are unique) from 232,265 full-length articles published between 1994 and 2004 in 78 journals, it is the largest repository in its class. GeneWays’s major strength is its use of an extensive collection of full-text articles. Other prime examples of large-scale interaction repositories are:

  1. iHOP [56], with 30,000 different genes and half a million sentences (and 500,000 website hits per month [57])
  2. PubGene [39], with 1,087,757 relationships (139,756 unique) and 13,712 genes
  3. PRIME, with 920,000 unique protein interactions [58]

At its core, GeneWays is GENIES, and it has built around GENIES the following set of modules for extra processing and display:

  • On the input side, a module fetches on-line, full-text articles and stores them in a repository.
  • The term tagging module was improved by the addition of a term classifier/disambiguator [59].
  • On the output side, a Simplifier module transforms the output from GENIES into simple triplet relations (e.g., “interleukin-2 binds interleukin-2 receptor”) following the GeneWays ontology [60], which is more suitable for analysis of regulatory networks.
  • The output from the simplifier is stored in the Interaction Knowledge Base, a relational database.
  • The knowledge base and other data elements product of the GeneWays pipeline (e.g., the terms extracted), can be displayed using a graphical tool called CUtenet that allows for network plotting as well as other data presentation formats.

Hence, GeneWays covers a number of steps including retrieval, processing, storage, and display, that allow end users to select their information of interest. GeneWays data can be used in multiple ways to furthering research of different issues in systems biology, text mining, and scientometrics [61,62,63,64,65].

1.4  Curation and evaluation

The idea of automatically curating a text-mined knowledge base was first proposed in the original GeneWays paper as an extra module called the AI curator. It was presented in the following way [35]:

“Note that the automatically generated knowledge base is of necessity noisy: the GeneWays system extracts some percentage of statements incorrectly, and, even among correctly extracted statements, we should expect redundancy and contradictions. Therefore, the database requires curation, a process in which the original statements are annotated with statements regarding confidence in the corresponding information. The traditional way to perform such curation is through manual labor of human experts-a monumental task even for the database at its current size of roughly 3 million redundant statements extracted from 150,000 articles. To reduce the manual work, we are implementing a Curator module that would allow GeneWays to compute the estimates of reliability automatically.”

Curation is considered a step in the process of ascertaining the truth of certain facts, especially for the scientist who is confronted with multiple, sometimes conflicting, pieces of information and who needs to make decisions within the knowledge pocket of her particular scientific specialization [66,63,65]. Curation also provides a way to assign a value to our degree of confidence about a fact within a continuous scale of truth. The value of truth assigned is an attempt to represent our limited ability to completely understand a text, or even the limited ability of the writer to express what she wants to say.

Evaluation is a central part of curation. Systems biology studies face the difficult task of measuring recall in a broad and intricate search space combined with the limitations of manual evaluation of precision. Many evaluations in the literature, including many of those cited herein, were not described in detail, which makes it hard to establish their characteristics. Often, they entail in-house evaluations in which unnamed experts follow protocols that are not detailed. This is understandable from the point of view that, in most cases, evaluations are considered a necessary, but not central, contribution. Friedman and Hripcsak [67] exposed a number of pitfalls in the task of evaluating NLP systems and defined 20 criteria to avoid them (see Table 1.4).

Minimizing Bias
1. The developer should not see the test set of documents.
2. If domain experts are used to determine the reference standard, they should not be developers of the system or designers of the study.
3. The developer should not perform the evaluation.
4. The NLP system should be frozen prior to the testing phase.
5. If generalizability of the processor is being tested, the developer should not know details of the study beforehand.
6. Ideally, the person designing the evaluation study should not be a developer of the system.
Establishing a Reference Standard
7. If domain experts are used to determine the reference standard, there should be a sufficient number to assess variability of the reference standard.
8. The test set should be large enough in that there is sufficient power to distinguish levels of performance.
9. The choice of the reference standard should be based on the objectives of the study (e.g. extraction capability vs. performance in an application).
10. If domain experts are used to determine the reference standard, the type of expert should be appropriate (e.g. radiologist vs. internist).
Describing the Evaluation Methods
11. The method used to determine the reference standard should be clearly described, particularly if domain experts were used.
12. The manner in which the test documents were chosen should be described.
13. Methods used to calculate performance measures should be clearly presented and if non-standard measures are used, they should be described.
Presenting Results
14. Performance measures should relate to the complete test set.
15. If human experts are used, inter-rater and intra-rater agreement should be given.
16. Confidence intervals should be given for all measures.
Discussing Conclusions
17. Limitations of the study should be discussed.
18. Results should be presented in light of requirements of the target application.
19. Overgeneralization of the results should be avoided.
20. An analysis of system failures should be given along with a discussion concerning the degree of difficulty of needed corrections.

Table 1.1: Criteria for Evaluating Performance of NLP Systems. [67]

In Sekimizu and colleagues’ seminal paper [36], two experts evaluated a random set of several hundred assertions with typical interaction verb connectors (activate, interact, encode, regulate, prevent, contain, inhibit, or bind). The researchers identified a different precision associated with each action type, a phenomenon also noted by Ono and colleagues [68]. Rindflesch and colleagues [37] used a test set of manually annotated sentences as gold standard to evaluate their application’s ability to identify the action type “bind”. Blaschke and colleagues [42] used two networks of known protein interactions as prediction targets for their system. They trained their system with selected Medline abstracts that included information about these networks, and then tested to see whether it had learned the network correctly. Stapley and Benoit [38] focused on relationships extracted from Medline documents with the MeSH term “DNA repair”. Reducing the search space to a specific domain eliminated the need to choose random documents from a repository (e.g. Medline), as was done initially in [36]. A limited evaluation, however, reduced the generalizability of the results. Thomas and colleagues [43] proposed, but did not implement, a scoring system to measure the level of certainty that a relationship has been well extracted by using as a template a pattern of co-occurrence. The scoring system would assign a score to each template based on three factors:

  1. Textual context (i.e., neighborhood of words and sentences).
  2. Degree of confidence that the term is a protein.
  3. Frequency in which the relationship appears.

However, they implemented a score based on the degree of likelihood that the terms of a given template are proper names. A template was considered more reliable if it identified relationships whose terms are proper names. Their rationale was that proper names are more likely to indicate protein names. Their scoring point scale (or scoring strategy) was, otherwise, arbitrary and it was used to make a preliminary filter of results. Proux and colleagues [44] used 200 sentences pre-evaluated by experts (evaluated as correct, incorrect, or undecided) as evaluation. This method became the most commonly used. Blaschke and Valencia [47] used word distance between terms and actions to yield estimated likelihoods of precision. They used a heuristic approach to compute probabilities for different word distances in order to give each result an estimated precision likelihood. Jenssen and colleagues [39] used actual micro-array expression data as a gold-standard for their gene co-occurrence text mining. The relationships found were compared to micro-array co-expression results.

In-house expert evaluation has been the most common method both for result evaluation and for gold-standard generation. Efforts like the Critical Assessment of Information Extraction (BioCreAtIvE) [69], which followed the lead of the successful Critical Assessment of PRediction of Interactions (CAPRI) [70], have raised awareness in the biomedical text-mining community about the use of common evaluation test sets and standards. Daraselia and colleagues [71] performed a manual evaluation supplemented by cross-comparisons with the DIP and BIND databases. These databases are manually curated repositories that use co-occurrences as a pre-screening filter. Chen and Sharp [72] went further in this approach and used a set of interactions selected from DIP as a prediction target for their system. Hakenberg and colleagues [73] created evaluation sets from articles referenced in DIP and the annotated BioCreAtIvE corpus. Koike and colleagues [58] used abstracts from GO term annotations. The main strength of evaluation techniques that use publicly available, manually curated corpora is that comparisons can be made between evaluation results of different applications.

The state of evaluation in the text mining of biomedical interactions sets the stage for the automatic curation project in Chapter 2, the aim of which is to go beyond existing evaluation schemes as so far described.

Chapter 2
Automatic curation of text-mined facts

…he will throughly purge his floor, and gather his wheat into the garner; but he will burn up the chaff with unquenchable fire.
Matthew 3:12 [74]

Current automated approaches for extracting biologically important facts from scientific articles are imperfect: while being capable of efficient, fast and inexpensive analysis of enormous quantities of scientific prose, they make errors. In order to emulate the human experts evaluating the quality of the automatically extracted facts, we have developed an artificial intelligence program (“a robotic curator”) that closely approaches human experts in the quality of distinguishing the correctly extracted facts from the incorrectly extracted ones.

Figure 2.1: Cocaine: the predicted accuracy of individual text-mined facts involving semantic relation stimulate. Each directed arc from an entity A to an entity B in this figure should be interpreted as a statement “A stimulates B”, where, for example, A is cocaine and B is progesterone. The predicted accuracy of individual statements is indicated both in color and in width of the corresponding arc. Note that, for example, the relation between cocaine and progesterone was derived from multiple sentences, and different instances of extraction output had markedly different accuracy. Altogether we collected 3,910 individual facts involving cocaine. So long as the same fact can be repeated in different sentences, only 1,820 facts out of 3,910 were unique. The facts cover 80 distinct semantic relations, out of which stimulate is just one example.

2.1  Introduction

Information extraction uses computer-aided methods to recover and structure meaning that is locked in natural-language texts. The assertions uncovered in this way are amenable to computational processing that approximates human reasoning. In the special case of biomedical applications, the texts are represented by books and research articles, and the extracted meaning comprises diverse classes of facts, such as relations between molecules, cells, anatomical structures, and maladies.

Unfortunately, the current tools of information extraction produce imperfect, noisy results. Although even imperfect results are useful, it is highly desirable for most applications to have the ability to rank the text-derived facts by the confidence in the quality of their extraction (as we did for relations involving cocaine, see Figure 2.1). We focus on automatically extracted statements about molecular interactions, such as small molecule A binds protein B, protein B activates gene C, or protein D phosphorylates small molecule E. (In the following description we refer to phrases that represent biological entities (such as small molecule A, protein B, and gene C) as terms, and to biological relations between these entities (such as activate or phosphorylate) as relations or verbs.)

Several earlier studies have examined aspects of evaluating the quality of text-mined facts, as explained in Section 1.4. For example, Sekimizu et al. and Ono et al. attempted to attribute different confidence values to different verbs that are associated with extracted relations, such as activate, regulate, and inhibit [36,68]. Thomas et al. proposed to attach a quality value to each extracted statement about molecular interactions [43], although the researchers did not implement the suggested scoring system in practice. In an independent study [47], Blaschke and Valencia used word-distances between biological terms in a given sentence as an indicator of the precision of extracted facts. In our present analysis we applied several machine-learning techniques to a large training set of 98,679 manually evaluated examples (pairs of extracted facts and corresponding sentences) to design a tool that mimics the work of a human curator who manually cleans the output of an information-extraction program.

2.2  Approach

Our goal is to design a tool that can be used with any information-extraction system developed for molecular biology. In this study, our training data came from the GeneWays project (specifically, GeneWays 6.0 database, [51,35]) and thus our approach is biased toward relationships that are captured by that specific system3. We believe that the spectrum of relationships represented in the GeneWays ontology is sufficiently broad that our results will prove useful for other information-extraction projects.

Our approach followed the path of supervised machine-learning. First, we generated a large training set of facts that were originally gathered by our information-extraction system, and then manually labeled as “correct” or “incorrect” by a team of human curators. Second, we used a battery of machine-learning tools to imitate computationally the work of the human evaluators. Third, we split the training set into ten parts, so that we could evaluate the significance of performance differences among the several competing machine-learning approaches.

Figure 2.2: Accuracy of the raw (non-curated) extracted relations in the GeneWays 6.0 database. The accuracy was computed by averaging over all individual specific information extraction examples manually evaluated by the human curators. The plot compactly represents both the per-relation accuracy of the extraction process (indicated with the length of the corresponding bar) and theabundance of the corresponding relations in the database (represented by the bar color). There are relations extracted with a high precision; there are also many noisy relationships. The database accuracy was markedly increased by the automated curation outlined in this study, see Figure 2.3.

Figure 2.3: Accuracy and abundance of the extracted and automatically curated relations. This plot represents both the per-relation accuracy after both information extraction and automated curation were done. Accuracy is indicated with the length of the relation-specific bars, while the abundance of the corresponding relations in the manually curated data set is represented by color. Here, the MaxEnt 2 method was used for the automated curation. The results shown correspond to a score-based decision threshold set to zero; that is, all negative-score predictions were treated as “incorrect.” An increase in the score-based decision boundary can raise the precision of the output at the expense of a decrease in the recall-see Figure 2.10.

2.3  Methods

2.3.1  Training data

With the help of a text-annotation company, ForScience Inc., we generated a training set of approximately 45,000 repeatedly-annotated unique facts, or almost 100,000 independent evaluations. These facts were originally extracted by the GeneWays pipeline, then were annotated by biology-savvy doctoral-level curators as “correct” or “incorrect,” referring to quality of information extraction. Examples of automatically extracted relations, sentences corresponding to each relation, and the labels provided by three evaluators are shown in Table 2.1.

Each extracted fact was evaluated by one, two, or three different curators. The complete evaluation set comprised 98,679 individual evaluations performed by four different people, so most of the statement-sentence pairs were evaluated multiple times, with each person evaluating a given pair at most once. In total, 13,502 statement/sentence pairs were evaluated by just one person, 10,457 by two people, 21,421 by three people, and 57 by all four people. Examples of both high inter-annotator agreement and low-agreement sentences are shown in Table 2.2.

The statements in the training data set were grouped into chunks; each chunk was associated with a specific biological project, such as analysis of interactions in Drosophila melanogaster. Pair-wise agreement between evaluators was high (92%) in most chunks4, with the exception of a chunk of 5,271 relations where agreement was only 74%. These relatively low-agreement evaluations were not included in the training data for our analysis5.

Figure 2.4: Sentence Evaluation Tool. Evaluators choose from different options. A triplet can be either correctly extracted, incorrectly extracted or the evaluator is “unable to decide”. Correctly extracted triplets may be hypothetical. Triplets may have been incorrectly extracted for several reasons: incorrect upstream term, incorrect downstream term, incorrect action verb (action type), missing or extra negation, wrong upstream vs. downstream order (upstream term is downstream or vice versa) or that the sentence does not support the action presented. Other possibilities are: sentence boundary error (e.g. two sentences were presented as one) or the action is incorrect biologically.

To facilitate evaluation, we developed a Sentence Evaluation Tool implemented in Java programming language by Mitzi Morris and Ivan Iossifov, Figure 2.4. This tool presented to an evaluator a set of annotation choices regarding each extracted fact; the choices are listed in Table 2.2. The tool also presented in a single window the fact itself and the sentence it was derived from. In the case a broader context was required for the judgment, the evaluator had a choice to retrieve the complete journal article containing this sentence by clicking a single button on the program interface.

For convenience of representing the results of manual evaluation, we computed an evaluation score for each statement as follows. Each sentence-statement score was computed as a sum of the scores assigned by individual evaluators; for each evaluator, 1 was added if the expert believed that the presented information was extracted incorrectly, and +1 was added if he or she believed that extraction was correct. For a set of three experts, this method permitted four possible scores: 3 (1,1,1), 1 (1,1,1), 1 (1,1,1), and 3. Similarly, for just two experts, the possible scores are 2 (1, 1), 0 (1,1), and 2(1,1).6

Sentence [Source] Extracted relation Evaluation(Confidence)
NIK binds to Nck in cultured cells. [76] nik bind nck Correct (High)
One is that presenilin is required for the proper trafficking of Notch and APP to their proteases, which may reside in an intracellular compartment. [77] presenilin required fornotch Correct (High)
Serine 732 phosphorylation of FAK by Cdk5 is important for microtubule organization, nuclear movement, and neuronal migration. [78] cdk5 phosphorylate fak Correct (High)
Histogram quantifying the percent of Arr2bound to rhodopsin-containing membranes after treatment with blue light (B) or blue light followed by orange light (BO). [79] arr2 bind rhodopsin Correct (Low)
It is now generally accepted that a shift from monomer to dimer and cadherin clusteringactivates classic cadherins at the surface into an adhesively competent conformation. [80] cadherin activate cadherins Correct (Low)
Binding of G to CSP was four times greater than binding to syntaxin. [81] csp bind syntaxin Incorrect (Low)
Treatment with NEM applied with cGMP madeactivation by cAMP more favorable by about 2.5 kcal/mol. [82] camp activate cgmp Incorrect (Low)
This matrix is likely to consist of actinfilaments, as similar filaments can be inducedby actin-stabilizing toxins (O. S. et al., unpublished data). [83] actin induce actin Incorrect (High)
A ligand-gated association betweencytoplasmic domains of UNC5 and DCC family receptors converts netrin-induced growth cone attraction to repulsion. [84] cytoplasmic domainsassociate unc5 Incorrect (High)

Table 2.1: Sentence examples.
A sample of sentences that were used as an input to automated information extraction (the first column), biological relations extracted from these sentences (either correctly or incorrectly, the second column), and the corresponding evaluations provided by 3 human experts (the third column). A high-confidence label corresponds to a perfect agreement among all experts; a low-confidence label indicates that one of the experts disagreed with the other two.

Term level
Upstream term is a junk substance
Action is incorrect biologically
Downstream term is a junk substance
Relation level
Correctly extracted
Sentence is hypothesis, not fact
Unable to decide
Incorrectly extracted
Incorrect upstream
Incorrect downstream
Incorrect action type
Missing or extra negation
Wrong action direction
Sentence does not support the action
Sentence level
Wrong sentence boundary

Table 2.2: List of annotation choices available to the evaluators. The term “action” refers to the type of the extracted relation. For example, in statement A binds B “binds” is the action, “A” is theupstream term, and “B” is the downstream term. Action direction is defined as upstream to downstream, and “junk substance” is an obviously incorrectly identified term/entity.

2.4  Mathematical background

2.4.1  Machine-learning algorithms

General framework

The objects that we want to classify, the fact-sentence pairs, have complex properties. We want to place each of them into one of two classes, correct or incorrect. In the training data, each extracted fact is matched to a unique sentence from which it was extracted, even though multiple sentences can express the same fact and a single sentence can contain multiple facts. The ith object (the ith fact-sentence pair) comes with a set of known features or properties that we encode into a feature vector, Fi:


Fi = (fi,1, fi,2, ¼, fi,n).

In the following description we use C to indicate the random variable that represents class (with possible values ccorrect and cincorrect), and F to represent a 1 ×n random vector of feature values (also often called attributes), such that Fj is the jth element of F. For example, for fact p53 activates JAK, feature F1 would have value 1 because the upstream term p53 is found in a dictionary derived from the GenBank database [85]; otherwise, it would have value 0.

Full Bayesian inference

The full Bayesian classifier assigns the ith object to the kth class if the posterior probability P(C=ck|F=Fi) is greater for the kth class than for any alternative class. This posterior probability is computed in the following way (a re-stated version of Bayes’ theorem).

P(C=ck|F=Fi)=P(C=ck) × P(F=Fi|C=ck)



In the real-life applications, we estimate probability P(F=Fi|C=ck) from the training data as a ratio of the number of objects that belong to the class ck and have the same set of feature values as specified by the vector Fi to the total number of objects in class ck in the training data.

In other words, we estimate the conditional probability for every possible value of the feature vector F for every value of class C. Assuming that all features can be discretized, we have to estimate


(v1 ×v2 ×¼vn 1) ×m

parameters, where vi is the number of discrete values observed for the ith feature and m is the number of classes.

Clearly, even for a space of only 20 binary features7 the number of parameters that we would need to estimate is (2201) ×2 = 2,097,150, which exceeds several times the number of data points in our training set.

Naïve Bayes classifier

The most affordable approximation to the full Bayesian analysis is the Naïve Bayes classifier. It is based on the assumption of conditional independence of features:

P(F2=fi,2|C=ck) ¼

Obviously, we can estimate P(Fj=fi,j|C=ck)’s reasonably well with a relatively small set of training data, but the assumption of conditional independence (Equation 2.5) comes at a price: the Naïve Bayes classifier is usually markedly less successful in its job than are its more sophisticated relatives.8

In an application with m classes and n features (given that the ith feature has vi admissible discrete values), a Naïve Bayes algorithm requires estimation of m ×åi=1,n (vi 1) parameters (which value, in our case, is equal to 4,208).

Figure 2.5: The correlation matrix for the features used by the classification algorithms.

The half-matrix below the diagonal was derived from analysis of the whole GeneWays 6.0 database; the half-matrix above the diagonal represents a correlation matrix estimated from only the manually annotated data set. The white dotted lines outline clusters of features, suggested by analysis of the annotated data set; we used these clusters in implementation of the Clustered Bayes classifier. We used two versions of the Clustered Bayes classifier: with all 68 features (Clustered Bayes 68), and with a subset of only 44 features, but higher number of discrete values allowed for non-binary features (Clustered Bayes 44). The Clustered Bayes 44 classifier did not use features 1, 6, 7, 8, 9, 12, 27, 28, 31, 34, 37, 40, 42, 47, 48, 49, 52, 54, 55, 60, 62, 63, and 65.

Middle ground between the full and Naïve Bayes: Clustered Bayes

We can find an intermediate ground between the full and Naïve Bayes classifiers by assuming that features in the random vector F are arranged into groups or clusters, such that all features within the same cluster are dependent on one another (conditionally on the class), and all features from different classes are conditionally independent. That is, we can assume that the feature random vector (F) and the observed feature vector for the ith object (Fi) can be partitioned into sub-vectors:

(F1, F2, ¼, FM), and
(fi,1, fi,2, ¼, fi,M),

respectively, where Fj is the jth cluster of features; fi,j is the set of values for this cluster with respect to the ith object, and M is the total number of clusters of features.

The Clustered Bayes classifier is based on the following assumption about conditional independence of clusters of features:

P(F2=fi,2|C=ck) ¼

We tested two versions of the Clustered Bayes classifier: one version used all 68 features (Clustered Bayes 68) with a coarser discretization of feature values; another version used a subset of 44 features (Clustered Bayes 44) but allowed for more discrete values for each continuous-valued feature, see legend to Figure 2.5.

Linear and quadratic discriminants

Another method that can be viewed as an approximation to full Bayesian analysis is Discriminant Analysis invented by Sir Ronald A. Fisher [86]. This method requires no assumption about conditional independence of features; instead, it assumes that the conditional probability P(F=Fi|C=ck) is a multivariate normal distribution.

P(F=Fi|C=ck)= e[1/2](Fimk)¢Vk1(Fimk)


(2p)n |Vk|

where n is the total number of features/variables in the class-specific multivariate distributions. The method has two variations. The first, Linear Discriminant Analysis, assumes that different classes have different mean values for features (vectors mk), but the same variance-covariance matrix, V = Vk for all k.9 In the second variation, Quadratic Discriminant Analysis (QDA), the assumption of the common variance-covariance matrix for all classes, is relaxed, such that every class is assumed to have a distinct variance-covariance matrix, Vk.10

In this study we present results for QDA; the difference from the linear discriminant analysis was insignificant for our data (not shown). In terms of the number of parameters to estimate, QDA uses only two symmetrical class-specific covariance matrices and the two class-specific mean vectors. For 68 features the method requires estimation of 2 ×(68 ×69)/2 + 2 ×68 = 4,828 parameters.

Maximum-entropy method

The current version of the maximum-entropy method was formulated by E.T. Jaynes [87,88]; the method can be traced to earlier work by J. Willard Gibbs. The idea behind the approach is as follows. Imagine that we need to estimate a probability distribution from an incomplete or small data set-this problem is the same as that of estimating the probability of the class given the feature vector, P(C=ck|F=Fi), from a relatively small training set. Although we have no hope of estimating the distribution completely, we can estimate with sufficient reliability the first (and, potentially, the second) moments of the distribution. Then, we can try to find a probability distribution that has the same moments as our unknown distribution and the highest possible Shannon’s entropy-the intuition behind this approach being that the maximum-entropy distribution will minimize unnecessary assumptions about the unknown distribution. The maximum-entropy distribution with constraints imposed by the first-order feature moments alone (the mean values of features) is known to have the form of an exponential distribution [89]:

exp æ
li,k fj,i ö

exp æ
li,l fj,i ö

and the maximum-entropy distribution for the case when both the first- and the second-order moments of the unknown distribution are fixed has the form of a multidimensional normal distribution [89]. The conditional distribution that we are trying to estimate can be written in the following exponential form:

P(C=ck|F=Fj) =
exp æ
li,k fj,i n
nx,y,k fj,x fj,y ö

exp æ
li,l fj,i n
nx,y,l fj,x fj,y ö

Parameters li,k‘s and nx,y,k‘s are k-class-specific weights of individual features and feature pairs, respectively, and in principle can be expressed in terms of the first and second moments of the distributions. The values of parameters in Equations 2.10 and 2.11 are estimated by maximizing the product of probabilities for the individual training examples.

We tested two versions of the maximum-entropy classifier. MaxEnt 1 uses only information about the first moments of features in the training data (Equation 2.10); MaxEnt 2 uses the set of all individual features and the products of feature pairs (Equation 2.11). To select the most informative pairs of features we used a mutual information approach, as described in the subsection dealing with classification features.

For two classes (correct and incorrect) and 68 features MaxEnt 1 requires estimation of 136 parameters. In contrast, MaxEnt 2 requires estimation of 4,828 parameters: weight parameters for all first moments for two classes, plus weights for the second moments for two classes. MaxEnt 2-v is a version of MaxEnt 2 classifier where the squared values of features are not used, so that the classifier requires estimation of only 4,692 weight parameters.

Figure 2.6: A hypothetical three-layered feed-forward neural network. We used a similar network with 68 input units (one unit per classification feature) and 10 hidden-layer units.

Feed-forward neural network

A typical feed-forward artificial neural network is a directed acyclic graph organized into three (or more) layers. In our case, we chose a three-layered network, with a set of nodes of the input layer, {xi}i=1, ¼, Nx, nodes of the hidden layer, {yj}j=1, ¼, Ny, and a single node representing the output layer, z1, see Figure 2.6. The number of input nodes, Nx, is determined by the number of features used in the analysis (68 in our case). The number of hidden nodes, Ny, determines both the network’s expressive power and its ability to generalize. Too small a number of hidden nodes makes a simplistic network that cannot learn from complex data. Too large a number makes a network that tends to overtrain-that works perfectly on the training data, but poorly on new data. We experimented with different values of Ny and settled on Ny = 10.

The values of the input nodes, {xi}i=1, ¼, Nx, are feature values of the object that we need to classify. The value of each node, yj, in the hidden layer is determined in the following way:


yj = F( wj,1 x1 + wj,2 x2 + ¼+ wj,Nx xNx),

where F(x) is a hyperbolic tangent function that creates an S-shaped curve:


F(x) = ex ex

ex + ex


and {wj,k} are weight parameters. Finally, the value of the output node, z1 is determined as a linear combination of the values of all hidden nodes:


z1 = a1 y1 + a2 y2 + ¼+ aNy yNy,

where {ak} are additional weight parameters. We trained our network, using a back-propagation algorithm [90], to distinguish two classes, correct and incorrect, where positive values of z1 corresponded to the class correct.

The feed-forward neural network that we used in our analysis can be thought of as a model with Nx ×Ny + Ny parameters (690 in our case).

Support vector machines

The Support Vector Machines (SVM, [91,92]) algorithm solves a binary classification problem by dividing two sets of data geometrically, by finding a hyperplane that separates the two classes of objects in the training data in an optimum way (maximizing the margin between the two classes).

The SVM is a kernel-based algorithm, where the kernel is an inner product of two feature vectors (function/transformation of the original data). In this study, we used three of the most popular kernels: the linear, polynomial and Rbf (radial basis function) kernels. The linear kernel KL(x1, x2) = áx1, x2 ñ is simply the inner product of the two input feature vectors; an SVM with the linear kernel searches for a class-separating hyperplane in the original space of the data. Using a polynomial kernel, KPd(x1, x2) = (1+áx1, x2 ñ)d, is equivalent to transforming the data into a higher-dimensional space and searching for a separating plane there.11 Finally, using an Rbf kernel, KRbfg(x1, x2) = eg ||x1x2 ||2, corresponds to finding a separating hyperplane in an infinite-dimensional space.

In the most real-world cases the two classes cannot be separated perfectly by a hyperplane, and some classification errors are unavoidable. SVM algorithms use the C-parameter to control the error rate during the training phase (if the error is not constrained, the margin of every hyperplane can be extended infinitely). In this study, we used the default values for the C-parameter suggested by the SVM Light tool. Table 2.3 lists the SVM models and C-parameter values that we used in this study.

Model Kernel Kernel parameter C-parameter
SVM (OSU SVM) Linear 1
SVM-t0 (SVM Light) Linear 1
SVM-t1-d2 Polynomial d=2 0.3333
SVM-t1-d3 Polynomial d=3 0.1429
SVM-t2-g0.5 Rbf g=0.5 1.2707
SVM-t2-g1 Rbf g=1 0.7910
SVM-t2-g2 Rbf g=2 0.5783

Table 2.3: Parameter values used for various SVM classifiers in this study.

The output of an SVM analysis is not probabilistic, but there are tools to convert an SVM classification output into “posterior probabilities,” see chapter by J. Platt in [93]. (A similar comment is applicable to the artificial neural network.)

The number of support vectors used by the SVM classifier depends on the size and properties of the training data set. The average number of (1 ×68-dimensional) support vectors used in 10 cross-validation experiments was 12,757.5, 11,994.4, 12,092, 12,289.9, 12,679.7, and 14,163.8, for SVM, SVM-t1-d2, SVM-t1-d3, SVM-t2-g0.5, SVM-t2-g1, and SVM-t2-g2 classifiers, respectively. The total number of data-derived values (which we loosely call “parameters”) used by the SVM in our cross-validation experiments was therefore, on average, between 827,614 and 880,270 for various SVM versions.

Method Implementation URL Number of parameters
Naïve Bayes this study, WEKA 4,208
Clustered Bayes 68 this study N/A 276,432
Clustered Bayes 44 this study N/A 361,270
Discriminant Analysis this study N/A 4,828
SVM OSU SVM Toolbox for Matlab 827,614
SVM-t* SVM light [94] 827,614 to 880,270
Neural Network Neural Network toolbox for Matlab N/A 690
MaxEnt 1 Maximum Entropy Modeling Toolkit for Python and C++ /s0450736/maxent_toolkit.html 136
MaxEnt 2 same as the MaxEnt 1 same as the MaxEnt 1 4,828
MaxEnt 2-v same as the MaxEnt 1 same as the MaxEnt 1 4,692
Meta-Classifier OSU SVM Toolbox for Matlab > 11,560

Table 2.4: Machine learning methods used in this study and their implementations.


We implemented the meta-classifier on the basis of the SVM algorithm (linear kernel with C=1) applied to predictions (converted into probabilities that the object belongs to the class correct) provided by the individual “simple” classifiers. The meta-method used 1,445 support vectors (1 ×7-dimensional), in addition to combined parameters of the seven individual classifiers used as input to the meta-classifier.


A summary of the sources of software used in our study is shown in Table 2.4.

2.4.2  Features used in our analysis

We selected 68 individual features covering a range of characteristics that could help in the classification, see Table 2.5. To capture the flow of information in a molecular interaction graph (the edge direction), in each extracted relation we identified an “upstream term” (corresponding to the graph node with the outgoing directed edge) and a “downstream term” (the node with the incoming directed edge): for example, in the phrase “JAK phosphorylates p53,” JAK is the upstream term, and p53 is the downstream term. Features in the group keywords represent a list of tokens that may signal that the sentence is hypothetical, interrogative, negative, or that there is a confusion in the relation extraction (e.g. the particle “by” in passive-voice sentences). We eventually abandoned keywords as we found them to be uninformative features, but they are still listed for the sake of completeness.

Group of features Feature(s) Values Number of features
Dictionary look-ups {Upstream, downstream} term can be found in {GeneBank, NCBI taxonomy, LocusLink, SwissProt, FlyBase, drug list, disease list, Specialist Lexicon, Bacteria, English Dictionary} Binary 20
Word metrics Length of the sentence (word count) Positive integer 1
Distance between the upstream and the downstream term Integer 1
Minimum non-negative word distance between the upstream and the downstream term Non-negative Integer 1
Distance between the upstream term and the action Integer 1
Distance between the downstream term and the action Integer 1
Previous scores Average score of relationships with the same {upstream term, downstream term, action} Real 3
Count of evaluated relationships with the same {upstream term, downstream term, action} Positive integer 3
Total count of relationships with the same {upstream term, downstream term, action} Positive integer 3
Average score of relationships that share the same pair of upstream and downstream terms Real 1
Total count of evaluated relationships that share the same pair of upstream and downstream terms Positive integer 1
Total count of relationships with both the same upstream and downstream terms Positive integer 1
Number of relations extracted from the same sentence Positive integer 1
Number of evaluated relations extracted from the same sentence Positive integer 1
Average score of relations from the same sentence Real 1
Number of relations sharing upstream term in same sentence Positive integer 1
Number of evaluated relations sharing upstream term in the same sentence Positive integer 1
Average score of relations sharing upstream term in same sentence Real 1
Relations sharing downstream term in the same sentence Positive integer 1
Evaluated relations sharing downstream term in the same sentence Positive integer 1
Average score of relations sharing downstream term in the same sentence Real 1
Number of relations sharing same action in the same sentence Positive integer 1
Number of evaluated relations sharing action in the same sentence Positive integer 1
Average score of relations sharing action in the same sentence Real 1
Punctuation Number of {periods, commas, semi-colons, colons} in the sentence Non-negative integer 4
Number of {periods, commas, semi-colons, colons} between upstream and downstream terms Non-negative integer 4
Terms Semantic sub-class category of the {upstream, downstream} term Integer 2
Probability that the {upstream, downstream} term has been correctly recognized Real 2
Probability that the {upstream, downstream} term has been correctly mapped Real 2
Part-of-speech tags {Upstream, downstream} term is a noun phrase Binary 2
Action is a verb Binary 1
Other Relationship is negative Binary 1
Action index Positive integer 1
Keyword is present Binary (not used)

Table 2.5: List of the features that we used in the present study. Dictionary lookups are binary features indicating absence or presence of a term in a specific dictionary. Previous scores are the average scores that a term or an action has in other relations evaluated. Term-recognition probabilities are generated by the GeneWays pipeline and reflect the likelihood that a term had been correctly recognized and mapped. Sharing of the same action (verb) by two different facts within the same sentence occurs in phrases such as A and B were shown to phosphorylate C. In this example, two individual relations, A phosphorylates C and B phosphorylates C, share the same verb, phosphorylate. Semantic categories are entities (semantic classes) in the GeneWays ontology (e.g. gene, protein,geneorprotein). Part-of-speech tags were generated by the Maximum Entropy tagger, MXPOST [95].

To represent the second-order features (pairs of features), we defined a new feature as a product of the normalized values of two features. We obtained the normalized values of features by subtracting the mean value from each feature value, then dividing the result by the standard deviation for this feature.

2.4.3  Separating data into training and testing: Cross-validation

To evaluate the success of our classifiers we used a 10-fold cross-validation approach, where we used [9/10] of data for training and [1/10] for testing. More precisely, given a partition of the manually evaluated data into 10 equal portions, we created 10 different pairs of training-test subsets, where we used each of the 10 equal data subsets in turn as testing data set, and used the larger remaining data subset as the training set. We then used 10 training-test set pairs to compare all algorithms.

2.4.4  Comparison of methods: Receiver operating characteristic (ROC) scores

To quantify and compare success of the various classification methods we used receiver operating characteristic (ROC) scores, also called areas under ROC curve [96].

An ROC score is computed in the following way. All test-set predictions of a particular classification method are ordered by the decreasing quality score provided by this method; for example, in the case of the Clustered Bayes algorithm, the quality score is the posterior probability that the test object belongs to the class correct. The ranked list is then converted into binary predictions by applying a decision threshold, T: All test objects with a quality score above T are classified as correct and all test objects with low-than-threshold scores are classified as incorrect. The ROC score is then computed by plotting the proportion of true-positive predictions (in the test set we know both the correct label and the quality score of each object) against false-positive predictions for the whole spectrum of possible values of T, then integrating the area under the curve obtained in this way, see Figure 2.7.

The ROC score is an estimate of the probability that the classifier under scrutiny will label correctly a pair of statements, one of which is from the class correct and one from the class incorrect [96]. A completely random classifier therefore would have an ROC score of 0.5, whereas a hypothetical perfect classifier would have an ROC score of 1. It is also possible to design a classifier that performs less accurately than would one that is completely random; in this case the ROC score is less than 0.5, which indicates that we can improve the accuracy of the classifier by simply reversing all predictions.

2.5  Results

The raw extracted facts produced by our system are noisy. Although many relation types are extracted with accuracy above 80%, and even above 90% (see Figure 2.2), there are particularly noisy verbs/relations that bring the average accuracy of the “raw” data to about 65%.12 Therefore, additional purification of text-mining output, either computational or manual, is indeed important.

The classification problem of separating correctly and incorrectly extracted facts appears to belong to a class of easier problems. Even the simplest Naïve Bayes method, had an average ROC score of 0.84 which improved to almost 0.95 with more sophisticated approaches. Judging by the average ROC score, the quality of prediction increased in the following order of methods: Clustered Bayes 68, Naïve Bayes, MaxEnt 1, Clustered Bayes 44, Quadratic Discriminant Analysis, artificial neural network, support vector machines, and MaxEnt 2/MaxEnt 2-v (see Table 2.8). The Meta-method was always slightly more accurate than MaxEnt 2, as explained in legend to Table 2.8 and shown in Figure 2.7.

Figure 2.7: Receiver-operating characteristic (ROC) curves for the classification methods that we used in the present study. We show only the linear-kernel SVM and the Clustered Bayes 44 ROC curves to avoid excessive data clutter.

Table 2.8 provides a somewhat misleading impression that MaxEnt 2 and MaxEnt 2-v are not significantly more accurate than their closest competitors (the SVM family), because of the overlapping confidence intervals. However, when we trace the performance of all classifiers in individual cross-validation experiments (see Figure 2.9) it becomes clear that MaxEnt 2 and MaxEnt 2-v outperformed their rivals in every cross-validation experiment. The SVM and artificial neural network methods performed essentially identically, and were always more accurate than three other methods: QDA, Clustered Bayes 44, and MaxEnt 1. Finally, the performance of the Clustered Bayes 68 and the Naïve Bayes methods was reliably the least accurate of all methods studied.

Figure 2.8: Correlation matrix. Comparison of a correlation matrix for the features (colored half of the matrix) computed using only the annotated set of data and a matrix of mutual information between all feature pairs and the statement class (correct or incorrect). The plot indicates that a significant amount of information critical for classification is encoded in pairs of weakly correlated features. The white dotted lines outline clusters of features, suggested by analysis of the annotated data set; we used these clusters in implementation of the Clustered Bayes classifier.

It is a matter of both academic curiosity and of practical importance to know how the performance of our artificial intelligence curator compares to that of humans. If we define the correct answer as a majority-vote of the three human evaluators (see Table 2.6), the average accuracy of MaxEnt 2 is slightly lower than, but statistically indistinguishable from humans (at the 99% level of significance, see Table 2.6; capital letters “A”, “L”, “S”, and “M” hide the real names of the human evaluators). If, however, in the spirit of Turing’s test of machine intelligence [97], we treat the MaxEnt 2 algorithm on an equal footing with the human evaluators, compute the average over predictions of all four anonymous evaluators, and compare the quality of the performance of each evaluator with regard to the average, MaxEnt 2 always performs slightly more accurately than one of the human evaluators.13 (In all cases we compared performance of the algorithm on data that was not used for its training; see Tables 2.7 and 2.6.)

The features that we used in our analysis are obviously not all equally important. To elucidate the relative importance of the individual features and of feature pairs, we computed the mutual information between all pairs of features and the class variable, (see Figure 2.8). The mutual information of class variable, C, and a pair of feature variables, (Fi,Fj) is defined in the following way (e.g., see [98]).



where function H(P[x]) is Claude E. Shannon’s entropy of distribution P(x) (see p. 14 of [99]), defined in the following way:


H(P) =
P(x) logP(x),

where summation is done over all admissible values of x. Figure 2.8 shows that the most informative standalone features, as expected, are those that contain information about the manually evaluated terms and relations of each type, and about properties of the sentence that was used to extract the corresponding fact. In addition, some dictionary-related features, such as finding a term in the LocusLink, are fairly informative. Some features, however, become informative only in combination with other features. For example, the minimum positive distance between two terms in a sentence is not very informative by itself, but becomes fairly useful in combination with other features, such as the number of commas in the sentence, or the length of the sentence (see Figure 2.8). Similarly, while finding a term in GenBank does not help the classifier by itself, the feature becomes informative in combination with syntactic properties of the sentence and statistics about the manually evaluated data.

Figure 2.9: Ranks of all classification methods used in this study in 10 cross-validation experiments.

Assignment of facts to classes correct and incorrect by evaluators is subject to random errors: Facts that were seen by many evaluators would be assigned to the appropriate class with higher probability than facts that were seen by only one evaluator. This introduction of noise affects directly the estimate of the accuracy of an artificial intelligence curator: If the gold standard is noisy, the apparent accuracy of the algorithm compared to the gold standard is lower than the real accuracy. Indeed, the three-evaluator gold standard, see Table 2.6, indicated that the actual optimum accuracy of the MaxEnt 2 classifier is higher than 88% percent. (The 88% accuracy estimate came from comparison of MaxEnt 2 predictions to the whole set of annotated facts, half of which were seen by only one or two evaluators, see Figure 2.10.) When MaxEnt 2 was compared with the three-human gold standard, the estimated accuracy was about 91% (see Table 2.6).

Evaluator Correct Incorrect Accuracy
[99% CI]
Batch A
A. 10,981 208 (11,189) 0.981410
[0.978014 0.984628]
L. 10,547 642 (11,189) 0.942622
[0.936902 0.948253]
M. 10,867 322 (11,189) 0.971222
[0.967111 0.975244]
MaxEnt 2 10,537 652 (11,189) 0.941728
[0.935919 0.947359]
Batch B
A. 9,796 430 (10,226) 0.957950
[0.952767 0.962938]
M. 9,898 328 (10,226) 0.967925
[0.963329 0.972325]
S. 9,501 725 (10,226) 0.929102
[0.922453 0.935556]
MaxEnt 2 9,379 847 (10,226) 0.917172
[0.910033 0.924115]

Table 2.6: Comparison of the performance of human evaluators and of the MaxEnt 2 algorithm. The first column lists all evaluators (four human evaluators, “A”, “L”, “M”, and “S”, and the MaxEnt 2 classifier). The second column gives the number of correct answers (with respect to the gold standard) produced by each evaluator. The third column shows the number of incorrect answers for each evaluator out of the total number of examples (in parentheses). The last column shows the accuracy and the 99% confidence interval for the accuracy value. The gold standard was defined as the majority among three human evaluators. Batches A and B were evaluated by different sets of human evaluators. We computed the binomial confidence intervals at the a-level of significance (a×100% CI) by identifying a pair of parameter values that separate areas of approximately [((1a))/2] at each distribution tail.

Evaluator Correct Incorrect Accuracy
(Total) [99% CI]
Batch A
A. 10,700 182 (10,882) 0.983275
[0.980059 0.986400]
L. 10,452 430 (10,882) 0.960485
[0.955615 0.965172]
M. 10,629 253 (10,882) 0.976751
[0.972983 0.980426]
MaxEnt 2 10,537 345 (10,882) 0.968296
[0.963885 0.972523]
Batch B
A. 9,499 363 (9,862) 0.963192
[0.958223 0.967958]
M. 9,636 226 (9,862) 0.977084
[0.973130 0.980836]
S. 9,332 530 (9,862) 0.946258
[0.940276 0.952038]
MaxEnt 2 9,379 483 (9,862) 0.951024
[0.945346 0.956500]

Table 2.7: C
omparison of human evaluators and a program that mimicked their work. The first column lists all evaluators (four human evaluators, “A”, “L”, “M”, and “S”, and the MaxEnt 2 classifier). The second column gives the number of correct answers (with respect to the gold standard) produced by each evaluator. The third column shows the number of incorrect answers for each evaluator out of the total number of examples (in parentheses). The last column shows the accuracy and the 99% confidence interval for the accuracy value. The gold standard was defined as the majority among three human evaluators and the MaxEnt 2 algorithm. Batches A and B were evaluated by different sets of human evaluators. We computed the binomial confidence intervals at the a-level of significance (a×100% CI) by identifying a pair of parameter values that separate areas of approximately [((1a))/2] at each distribution tail.

Figure 2.10: Values of precision, recall and accuracy of the MaxEnt 2 classifier plotted against the corresponding log-scores provided by the classifier. Precision is defined as [(true positives)/(true positives+false positives)], recall is defined as [(true positives)/(true positives+false negatives)], and accuracy is defined as [(true positives + true negatives)/(true positives+true negatives + false positives+false negatives)]. The optimum accuracy was close to 88%, and attained at score threshold slightly above 0. We can improve precision at the expense of accuracy: For example, by setting the threshold score to 0.6702 we can bring the overall database precision to 95%, which would correspond to a recall of 77.91%12 and to an overall accuracy of 84.18%.

2.6  Discussion

As evidenced by Figures 2.2 and 2.3, the results of our study are directly applicable to analysis of large text-mined databases of molecular interactions: We can identify sets of molecular interactions with any pre-defined level of precision (see Figure 2.10). For example, we can request from a database all interactions with extraction precision 95% or greater, which would result in the case of the GeneWays 6.0 database in recall of 77.9%.14 However, we are not forced to discard the unrequested lower-than-threshold-precision interactions as we must the chaff separated from wheat in the epigraph to this article. Intuitively, even weakly supported facts can be useful in interpreting experimental results, and may gain additional support when studied in conjunction with other related facts (see Figure 2.1 for examples of weakly supported yet useful facts, the accuracy predictions were computed using the MaxEnt 2 method). We envision that, in the near future, we will have computational approaches, such as probabilistic logic, that allow us to use weakly supported facts for building a reliable model of molecular interactions from unreliable facts (paraphrasing John von Neumann’s “synthesis of reliable organisms from unreliable components” [100]).

Experiments with any standalone set of data generate results insufficient to allow us to draw conclusions about the general performance of different classifiers. Nevertheless, we can speculate about the reasons for the observed differences in performance of the methods when applied to our data. The modest performance of the Naïve Bayes classifier is unsurprising: We know that many pairs of features used in our analysis are highly or weakly correlated (see Figures 2.8 and 2.5). The actual feature dependences violate the method’s major assumption about the conditional independence of features. MaxEnt 1, which performed significantly more accurately than the Naïve Bayes in our experiments, but was not as efficient as other methods, takes into account only the class-specific mean values of features; it does not incorporate parameters to reflect dependencies between individual features. This deficiency of MaxEnt 1 is compensated by MaxEnt 2, which has an additional set of parameters for pairs of features leading to a markedly improved performance.15

Our explanation for the superior performance of the MaxEnt 2 algorithm with respect to the remainder of the algorithms in the study batch is that MaxEnt 2 requires the least parameter tweaking in comparison to other methods of similar complexity. Performance of the Clustered Bayes method is highly sensitive to the definition of feature clusters and to the way we discretize the feature values-essentially presenting the problem of selecting an optimal model from an extensive set of rival models, each model defined by a specific set of feature clusters. Our initial intuition was that a reasonable choice of clusters can become clear from analysis of an estimated feature-correlation matrix. We originally expected that more highly correlated parameters would belong to the same cluster. However, the correlation matrices estimated from the complete GeneWays 6.0 database and from a subset of annotated facts turned out to be rather different-see Figure 2.5-suggesting that there are conflicting groups of highly correlated features. In addition, analysis of mutual information between the class of a statement and pairs of features (see Figure 2.8) indicated that the most informative pairs of features are often only weakly correlated. It is quite likely that the optimum choice of feature clusters in the Clustered Bayes method would lead to a classifier performance accuracy significantly higher than that of MaxEnt 2 in our study, but the road to this improved classifier lies through a search in an astronomically large space of alternative models.

Similar to optimizing the Clustered Bayes algorithm through model selection, we can experiment with various kernel functions in the SVM algorithm, and can try alternative designs of the artificial neural network. These optimization experiments are likely to be computationally expensive, but are almost certain to improve the prediction quality. Furthermore, there are bound to exist additional useful classification features waiting to be discovered in future analyses. Finally, we speculate that we can improve the quality of the classifier by increasing the number of human evaluators who annotate each data point in the training set. This would allow us to improve the gold standard itself, and, with luck, would lead to develop a computer program that performs the curation job consistently at least as accurately as an average human evaluator.

Method ROC score ± 2s
Clustered Bayes 68 0.8115 ±0.0679
Naïve Bayes 0.8409 ±0.0543
MaxEnt 1 0.8647±0.0412
Clustered Bayes 44 0.8751±0.0414
QDA 0.8826±0.0445
SVM-t0 0.9203±0.0317
SVM 0.9222±0.0299
Neural Network 0.9236±0.0314
SVM-t1-d2 0.9277±0.0285
SVM-t2-g2 0.9280±0.0285
SVM-t1-d3 0.9281±0.0280
SVM-t2-g1 0.9286±0.0283
SVM-t2-g0.5 0.9287±0.0285
MaxEnt 2 0.9480 ±0.0178
MaxEnt 2-v 0.9492±0.0156

Table 2.8: The receiver operator characteristic (ROC) scores (also called the area under the ROC curve) for methods used in this study, with error bars calculated in 10-fold cross-validation. We evaluated the Meta-method on a smaller set of data, so did not include its results in this Table. (The estimated ROC score for the Meta-method was 0.9456 ± 0.0076; it performed better than MaxEnt 2 in each cross-validation experiment, data not shown).

Chapter 3
Overview of biomedical term recognition and classification

3.1  Introduction

The term recognition and classification project was designed to address shortcomings in GeneWays that were identified in the curation project described in Chapter 2. One of the findings was that terms that were not reliably recognized produced a decrease in the performance of GeneWays. The goal, then, was to improve on the term tagging [55] and disambiguation [59] stages of the current GeneWays version to improve results. The framework followed was that proposed by Krauthammer and Nenadic [101], which divides the term identification task into three stages: term recognition, term classification, and term mapping. This introduction will present the previous work in term recognition and classification that influenced the project described in Chapter 4, with special focus on biomedical applications.

3.1.1  Term recognition

Automatic term recognition (ATR) grew from the fields of information retrieval (IR) and indexing [102]. The goal of indexing was to improve IR searches by focusing them on a limited number of surrogate descriptors instead of on full document text. The surrogates often were lexical units-like words of a certain type-that were used to build an index representing a document. Thus, searches could be centered on selected relevant words instead of all words. This approach is similar conceptually to the keywords included in library index cards. For example, a classical automatic indexing technique is to exclude from an index words that are too frequent (also called stop words, like the, a, and of), which do not add discriminatory power to a search because most documents include them. The first work in automatic indexing dates back to the late 1950s [103]. Early automatic indexing work is based on single-word index keywords. At the beginning of the 1970s the first multi-word expression indexes are developed.

ATR is a natural continuation for indexing. In its simplest expression, it brings an additional restriction to the task: that the surrogates extracted from a document belong to a specific domain. It is in the context of ATR that terms are used for indexing [104], and one of the early focuses of ATR was specialized terminologies created automatically from text. ATR overcomes limitations in single-word automatic indexing. Many objects or concepts often are written using more than one word (e.g., “United States of America”), if indexing is limited to single words (“United”, “States”, “of”, or “America”) important information is lost. Multi-word indexing that is not based on terms has its pitfalls, though, such as identifying groups of words of uncertain value. In multi-word indexing there might be little conceptual difference between using “States of America” or “United States of America” as the index, but clearly the two expressions are of different usefulness. Terms are important because they are semantically loaded and thus allow for more effective retrieval given the semantic interests typically expressed in many searches (e.g., retrieve all documents that deal with “network engineering”). Terms are understood as expressions that may be included in a specialized terminology-a vocabulary relative to a domain.

The two main approaches to ATR have been statistical and linguistic. Statistical approaches rely on analysis of the tendency of words to appear next to, or separate from, each other, in the same document or in different documents, and within certain grammatical settings (e.g., as adjective + noun). For example, the fact that two words appear next to each other more often than expected (a bigram) may hint that they belong to the same term. Linguistic approaches rely on the grammatical and syntactical patterns that appear in term formation. These patterns point out relationships among words that together may form a term. The noun phrase is considered a basic unit for term analysis (an example of noun phrase: “the red horse car”); it is a syntactic unit typically organized around a noun (the head noun, in this case, “car”). The words that are members of a noun phrase are modifiers of the head noun. These words often are adjectives (“red”), determinants (“the”), or other nouns (“horse”) that appear beside the head noun. Noun phrases can be determined by syntactic parsing. Linguistic and statistical analyses can be combined for improved results.

3.1.2  Named entity expressions

The pioneering Message Understanding Conference (MUC) series sponsored by DARPA between 1987 and 1997 aimed to improve information extraction methods by presenting different tasks open to competition. The format of the competitions encouraged the standardization of evaluation that has become more and more commonplace in computational linguistics. A named entity task was presented in MUC-6 in 1995 [105,106] and, again, in MUC-7. The challenge was to recognize three different expression types in a corpus: temporal expressions (date); number expressions (money, percent); and entity names. The contenders were scored on correct and partial matches to generate an overall score. The entity name recognition subtask involved recognition of names of people, organizations, and geographic locations. In the context of MUC-6 and MUC-7, entity name referred to what is more commonly called a proper name (e.g., Coca-Cola, James, Hollywood) [107]. This subtask became popularized as named entity recognition (NER) and its meaning was generalized to be recognition of any terms of interest, not necessarily the ones that are usually understood as being proper names16. NER combined term recognition and classification into one subtask, which not only brought together techniques from both fields but also spawned a new breed of studies. MUC’s NER promoted further work in term recognition and classification in other domains and applications, such as the multilingual entity task workshops (MET-1 and MET-2) [108]. One of the innovations of MUC’s NER was the scoring competition model using a tagged corpus as gold standard, which was replicated later in other competitions such as BioCreAtIvE I and II [69]. Its success-and perhaps its demise-was the high level of accuracy attained, which made it possible to declare the subtask “solved”, with an F-measure score of 94%97% [107].

3.1.3  Term classification

Term classification has received much less attention than term recognition. The first work in the field, which dates to the late 1960s and early 1970s [109,110,111], was devoted to classifying indexing keys to improve information retrieval. The task was named “automatic term classification”, which was akin to its sibling “automatic term recognition”.

While word sense disambiguation (WSD) might be considered a term classification subtask, or at least a type of term classification, it has a much older pedigree that dates to the dawn of computational linguistics because of its applications in machine translation or information retrieval [112]. As a result, many of the features and techniques used in WSD were used later for term classification [113,114]. Features used include [114]:

  1. Part of Speech (POS).
  2. Morphology.
  3. Collocations.
  4. Semantic word associations.
        a. Taxonomical organization.
        b. Situation.
        c. Topic.
      d. Argument-head relation.
  5. Syntactic cues.
  6. Semantic roles.
  7. Selectional preferences.
  8. Domain.
  9. Frequency of senses.
  10. Pragmatics.

New interest in term classification arose with the inception of the NER task. The classes included in MUC-6 were carefully defined to avoid ambiguities, like distinguishing when “White House” belongs to the class ENTITY or the class LOCATION. Features for classification where devised that were included along others for boundary recognition. The field has often been called named entity classification (NEC), even if it does not always deal with named entities; this is the same thing that happened with term recognition.

Since MUC’s NER originated, term classification has gone in several directions:

  • Term recognition and classification

    Term classification that goes hand-in-hand with term recognition, tackling NER-style problems.

  • Term classification

    Separated from term recognition if, maybe, using training/testing examples from the same NER corpora but devising two independent tasks [115,116,117].

    • Large-scale

      With classes sometimes numbering in the hundreds, evaluation is of a different nature and is oriented towards population of large ontologies [118,119,120].

    • Sub-categorization

      Also called fine-grained classification (e.g., dividing the term class PERSON into the classes ATHLETE, POLITICIAN/GOVERNMENT, CLERGY, BUSINESSPERSON, ENTERTAINER/ARTIST, LAWYER, DOCTOR/SCIENTIST, and POLICE [121,122,123]).

The relative indifference in research towards term classification outside named entities might come from the fact that the domains commonly involved in the NER tasks do not have such a rich and evolving vocabulary as does the biomedical domain. There is not a strong need for term classification in situations where most terms are present in dictionaries. Outside proper nouns, only domains such as chemistry, biology, or medicine have dictionaries or terminologies that are rendered quickly obsolete and inflexible by the fast-paced creation of new terms and term variations.

3.1.4  Biomedical term recognition and classification

NER’s influence on biomedical information extraction appeared in 1998 [124]. Given the lack of previous work in biomedical term recognition 17 and classification and the groups that began to work on it, it seems likely that text mining was a main driving force behind this growth. Although many researchers have used the term NER in their projects, this has been a misnomer (see for example the use of “Bio-NER” in [126]) and a legacy of MUC influence. Term recognition and classification in biomedicine is similar to NER in that it attempts to recognize and classify a limited number of classes (but usually just one) in a corpus. Unlike NER, however, it does not deal with proper nouns but with terms. This narrow focus produces precisely class-tailored strategies for different term classes.

Another influence from MUC’s NER is that the number of tagged corpora available, at least for molecular biology, has been increasing (e.g., Genia [127], Yapex [128]), and competitions such as the Bio-Entity Recognition Task in the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA) [129], and the Critical Assessment of Information Extraction in Biology (BioCreAtIvE-1 (2004) and BioCreAtIvE-2 (2006), both with their own corpus) workshop [130]. Biomedical term recognition has adopted many of the techniques used in MUC’s NER [131,132], including a fusion of the term recognition and classification stages (what Lee and colleagues called “one-phase NER” [133]). However, the differences prompted Takeuchi and Collier [134] to call it “extendednamed entity task (NE+).” Work in term recognition and classification that predates MUC’s NER approach has been influential for biomedical term recognition as well (see, for example, the similarities between [135] and [136]).

While some researchers have declared NER to be solved, this is not the case for biomedical term recognition and classification (e.g., “Although named entity recognition might be regarded a solved problem in some domains, it still poses a significant challenge in others.” [128]). The proper names considered in MUC’s NER are more limited in variation compared to biomedical terminology. The vocabulary in the biomedical domain is formidable [137], open [138] and growing; few rules are followed [124]; term variability is high [139]; no established rules exist for term boundary definition18[140,141], and class ambiguity is higher than in other domains [132]. Perhaps the best proof of its higher complexity is that MUC algorithms adapted to biomedical text have produced lower results.

Biomedical term recognition and classification faces terminology issues such as:

“For example, there is a gene name “bride of sevenless” (FlyBase ID FBgn0000206) with its acronym “boss”, as well as a protein that has been named after a Chinese breakfast noodle “yotiao” (Swiss-Prot ID Q99996). Even if biologists start to use exclusively “well-formed” and approved names, there are still a huge number of documents containing “legacy” and ad hoc terms.” [101]

“For example, we have fourways to tag the name in the phrase `yeast YSY6 protein’: `yeast YSY6 protein’, `yeast YSY6′, `YSY6 protein’ or `YSY6′. This ambiguity implies that annotators may include yeast today and may exclude it a year later, unless given some `annotation rules’. […]

To make things worse, protein names are often derived from descriptive terms (signal transducer and activator of transcription, STAT) and only later become accepted by the research community through repetition (STAT-4). Protein names also overlap with gene names (myc-c gene and myc-c protein), cell cultures (CD4+-cells and CD4 protein), and may be rather similar to chemical compounds (Caeridin and Cantharidin).” [141]

“The choice of a gene name can have unforeseen consequences in addition to infringement of trademark (“Pokemon blocks gene name” Nature 438, 897; 2005). The quirky sense of humour that researchers display in choosing a gene name often loses much in translation when people facing serious illness or disability are told that they or their child have a mutation in a gene such as Sonic hedgehog, Slug or Pokemon.

As with the acronym CATCH22 (from `cardiac anomaly, T-cell deficit, clefting and hypocalcaemia’) for chromosome 22q11.2 microdeletions, which was abandoned because of its no-win connotations (J. Burn J. Med. Genet. 36, 737–738; 1999), researchers need to be mindful when naming genes and syndromes.” [142]

“1. Authors often use the original words instead of abbreviations, change letter cases, and ignore implicit name generating rules.

  • epidermal growth factor receptor or EGF receptor or EGFR
  • cycline D1-cdk4 complex or cycline D1-Cdk4 complex
  • c-Jun or c-jun or c jun

2. Below, the name explains its function.

  • the Ras guanine nucleotide exchange factor Sos
  • the Ras guanine nucleotide releasing protein Sos
  • the Ras exchanger Sos
  • the GDP-GTP exchange factor Sos
  • Sos(mSos), a GDP/GTP exchange protein for Ras” [124]

Biomedical term recognition stresses the importance of morphological features (letter case, numbers, Greek letters, hyphens, etc.) [143,144] and infixes for term recognition, as a result of the formation patterns in some term classes in biomedicine. Different methodological strategies have been used for term recognition and classification ranging from straightforward dictionary matching to black box setups based on machine learning. Here they are described separately, although they more commonly are used in conjunction.

Dictionary matching

Matching dictionary entries to text is a simple method for term recognition and classification. The coverage limitations of this method are familiar: Dictionaries have gaps, they do not keep pace with the state-of-the-art, and they do not record term variation properly. Dictionaries also have limited in precision because a partial match does not ensure a true positive. Moreover, they are not helpful for disambiguation. Dictionary matching is used more often for feature generation rather than for term recognition technique, as explained in Section 3.1.4.

An improvement over simple matching is increasing the matching flexibility to some extent 19. Dictionary matching can be combined with additional information for improved matching (see, for example, [46]). Krauthammer and colleagues [55] used an innovative approach to matching using a BLAST-inspired algorithm [54] that performs short matches between the text and dictionary terms. These matches are later extended to find the matches that lie above a certain significance threshold. Morgan and colleagues [145] used suffix tree and longest-extent pattern matching on filtered abstracts similar to [146]. Yamamoto and colleagues instead used morphemes as basic units for matching [147].


A syntactical model of term recognition and classification is very helpful in reducing the search space and anchoring term words. Strategies used are influenced by previous term recognition work (see Section 3.1.1) in part of speech tagging and shallow parsing, but not in deep parsing. Noun phrases [131] and head nouns [148] sometimes are used as term placeholders or anchors. POS tags are integral features of many models, including Markov-based machine learning models ([143], see Section 3.1.4).

Models based on “core terms” are a departure from non-biomedical term recognition and classification models. Originally proposed by [124], core terms are words that contain capital letters, numerical figures, and special symbols characteristic of protein names. Terms are reconstructed around the “core terms” following several rules. Hanisch and colleagues [149] proposed a more elaborated model (see Table 3.1).

Name Description Examples
Modifier Semantic-modifying tokens receptor, inhibitor
Non-descriptive Annotating tokens fragment, precursor
Specifier Numbers and Greek letters 1, V1, alpha, gamma
Common Common English words and, was, killer
Delimiter Separator tokens ( ) , . ;
Standard Standard tokens TNF, BMP, IL

Table 3.1: Definition of token classes with differing semantic significance [149].

Syntactical strategies can be used in conjunction with additional features to filter terms that are not of interest.

Rule-based methods, probabilities and statistics

The first method used in biomedical term recognition was a rule-based recipe of steps for recognizing genes and proteins on the SH3 domain and signal transduction abstracts [124]. Although results were good, this technique’s lack of flexibility led to other efforts to make it more general [128,150]. The steps involved a syntactic model that was increasingly refined with statistics and probability filtering. Hou and Chen [151] used collocation statistics to enhance the results in [124] and [128]. A suffix-matching and extension rule-based grammar was used by [152,145].

Nobata and colleagues [131] used a probability method based on Naïve Bayes (see Section 2.4.1 on Naïve Bayes) and decision trees for term classification. Tanabe and Wilbur used Naïve Bayes with different statistical and POS features [153,154]. For word sense disambiguation (see Section 3.1.4), Hatzivassiloglou and colleagues [59] opted for three different methods: Naïve Bayes learning, decision trees, and inductive rule learning.

Machine learning

Several machine learning techniques have been used in the context of biomedical term recognition and classification. While techniques based on sequential state chains might be considered a better fit for a task involving sequential word tokens, support vector machines (SVMs) have been applied more often ([155,156,148,133,141,157,158], see Section 2.4.1). The common problem facing SVM implementation is that the data are very unbalanced-a given text contains many more words that do not belong to a term than words that do. To address this issue, different filtering methods have been used to reduce the search space, they eliminate words that are not part of a term using POS information, dictionaries, or n-gram statistics, and other information that represents a syntactic understanding of terms. The post-processing counterpart of filtering is extension, which consists of recovering words that may have been erroneously filtered to reconstruct terms. A SVM method without filtering/extension also has been implemented using a sliding window [157].

The prediction model for SVMs is based on B/I/O notation that is typical of simple sequential Markov models. Words that do not belong to a term are labeled O, whereas words that belong to a term are labeled B or I followed by the class. B stands for the first word of a term and I for an intermediate word. For example, the sequence “accurate initiation of transcription by RNA polymerase II”, can be labeled for the class protein as “accurate/O initiation/O of/O transcription/O by/O RNA/B-PROTEIN polymerase/I-PROTEIN II/I-PROTEIN” [133].

Sequential Markov model machine learning algorithms such as hidden Markov models ([159,145,160,161], see Section 3.2), maximum entropy Markov models (MEMM) ([162,156], see Section 2.4.1 on maximum entropy), and conditional random fields ([163,164,126,165], see Section 3.2) are a closer fit for addressing the task, although they also face the difficulties posed by an unbalanced label sequence. A different approach is integrating different models [166].

Word sense disambiguation

WSD has been closely linked to term classification. WSD is necessary in instances where a term belongs to two or more classes or terminologies. Because morphological, infix, and string matching features do not differ between ambiguous terms, deciding which class is the correct one entails contextual analysis. WSD has a long tradition in natural language processing (see also Section 3.1.3), and this tradition has been adapted to biomedical text in recent times [167,59,168,169,170,171,132,172,173,174]. There is even a biomedical corpus specialized in WSD [175]. As pointed out in [59,132,176], WSD is a harder task in biomedical texts compared to other texts such as news feeds; the principle of “one sense per discourse” does not hold as strongly in the former [132]. Gale and colleagues proposed the concept of “one sense per discourse” in an influential study of the persistence of a word sense of an ambiguous word in a text [177]. They discovered that in 98% of cases ambiguous words were used in only one sense within a text. This finding has been very influential, although it also has been criticized for their use of disambiguation categories that were too coarse-grained [178]. Another study quantified sense persistence in a text to be about 70% [179] for finer-grained categories. In biomedical text, sense persistence has been estimated to be 60% [132].

“The biology domain offers a prime example of this multiplicity of meanings, since every protein has an associated gene with often the same name. Further, genes and their transcripts (mRNA, rRNA, tRNA and the like) often share the same name as well. Often an article will refer to the protein, gene, and RNA senses of a term in close proximity, relying on the reader’s expertise and the surrounding context for disambiguation. For example, SBP2 is listed as a gene/protein in the GenBank […] database. In one of our source articles […] we find the following sentences:

  • `By UV cross-linking and immunoprecipitaion, we show that SBP2 specially binds selenoprotein mRNAs both in vitro and in vivo.’
  • `The SBP2 clone used in this study generates a 3173 nt transcript (2541 nt of coding sequence plus a 632 nt 3′ UTR truncated at the plyadenylation site).’

In the first sentence the highlighted occurrence of SBP2 is a protein, while in the second sentence is a gene.” [59]

Moreover, in the biomedical domain word sense might be harder for humans to distinguish. The pairwise agreement between human annotators for classification of gene and protein names has been measured to be about 78% [59,176], compared to 88100% for generic word senses [180,176].

Abbreviation or acronym resolution is a special case of WSD, for which a specific set of techniques has been developed [181,182,183,184,185,186,187]. The processing entails mapping an abbreviation to a definition for the purpose of disambiguating its meaning. Definitions and abbreviations may appear appositionally (e.g., the abbreviation DNA and the definition desoxyribonucleic acid in: Deoxyribonucleic acid (DNA)), but abbreviations may be found anywhere in a text, as definitions commonly are written only the first time the abbreviation is used. A third case occurs when the author considers that an abbreviation is so well known within a scientific field (e.g., DNA) that the definition is unnecessary, in this case it must be searched in a different text.

Although abbreviation resolution faces challenges similar to those of other biomedical text-mining tasks (e.g., large, open vocabulary; few rules; plurality of domains), it actually is a success story and almost a solved problem. This is, perhaps, because it is a well-defined task in terms of inputs and outcomes. Okazaki and Ananiadou [185] reported 99% precision and 8295% recall. Yu and colleagues [187] reported 92% precision and 91% coverage.

Features for machine learning

Two studies have shown how classical features compare in helping to classify and recognize term classes [144,188]. Torii and colleagues [144] enumerated four common feature types:

  1. F-term: Fukuda and colleagues [124,128] proposed the use of functional terms (F-terms), which are words that immediately follow a term and represent a strong clue to deciding its class and one of its boundaries (e.g., in “EGF receptor“, the word “receptor” indicates that EGF is a protein). First efforts were based on lists of manually selected F-terms but automatic, probabilistic approaches were developed later [144,148]. F-term is a particular case of a contextual feature. Its popularity is perhaps driven by its power and simplicity to be characterized and codified. Rindflesch and colleagues [46] talked more broadly of signal words, such as cell, clone, line, and cultured for the class cell and activated, expression, gene, and mutated for the class gene.
  2. Suffix: Suffixes are especially useful for classes in which word endings have standard meanings associated with them. This is often the case with chemical names (e.g., -ose, -ide, -ite, -ate) or protein names (e.g., -ase) [144]. Suffix is a particular case of a substring feature, which would include prefix and infix.
  3. String matching: Matching a sequence of words to an entry in a specialized dictionary (or terminology) can give a strong clue about the term’s class (e.g., finding that a term is the name of a protein in the protein database SwissProt). Dictionary lookup, however, has limitations for term matching for several reasons (see also Section 3.1.4). One of them is that small variations in a term’s form may hamper recognition (e.g., alpha-glucose vs. a-glucose). To avoid this, more flexible matching algorithms have been devised, using a mixture of features for similarity scoring. A comparison of matching algorithms can be seen in [189,190].
  4. Context: Context is generally understood to mean the words that surround a term in the text where the term appears. Often, context is limited to a window of several words that precede and follow a term. Context has been shown to be a powerful indicator of a term class, as the words used tend to differ in the surroundings of a given term depending on its class [191] (e.g., the verbs used are different [192]).

Character-level (also known as ortographical or morphological) features are another important type of features. This is a motley set of features about a term’s form that can often be encoded with binary values and have the potential to differentiate terms that belong to different classes. No established list of common morphological features exists, but similarities abound in the choices researchers make. Table 3.2 shows an example of a list used by [143]. Morphological, infix, and string matching features have been called internal evidence [193] (or word-internal information [194]), unlike F-terms and other contextual information, which are called external evidence.

Feature Example
GreekLetter kappa
CapsDigitHyphen Oct-1
CapsAndDigits STAT1
SingleCap B
LettersAndDigits p105
LowCaps pre-BI
OneDigit 2
TwoCaps EBV
InitCap Sox
HyphenDigit 95-
LowerCase kinases
Punctuation (
DigitSequence 98401159
TwoDigit 37
FourDigit 1997

Table 3.2: Morphologic binary feature values with examples as used in [143]. Although no single list of agreed-upon features exists, they tend to overlap and are tuned for similar cues depending on the term classes being considered.

3.2  Mathematical Background

3.2.1  Recognition and classification framework

The classification framework presented in Section 2.4.1 must be extended to address sequential and multi-class assignation problems. Our sequential problem involves a sequence of tokens t1, t2, ¼, tnwith feature associated vectors F1, F2, ¼, Fn, for which we identify subsequences Tk = (ti, ti+1, ¼, tj) that are potential terms. These subsequences might be arranged in a nested fashion. A term Tk is nested when all of its tokens are included in another term Tl, or


$l | Tk Ì Tl.

A term Tk is a parent if it is not nested, namely,


there is no l | Tk Ì Tl.

We break down the sequential task into two parts: identifying parent terms, and identifying nested terms. As explained in Section 4.2.3, our approach is to filter tokens by considering only those that are part of noun phrases. Some filtered tokens are reconsidered afterwards when joining noun phrases. For parent terms, tokens are classified into the classes cterm and cnonterm, with the labels I and O, respectively. Nested tokens may present themselves in more complex arrangements, and hence the list of labels is longer:

  • B: beginning of term
  • I: intermediate
  • E: end of term
  • U: unary (one-token) term
  • UE: unary term and end of term
  • UB: unary term and beginning of term
  • O: non-term

The label U also is used in place of a label UI. The subsequences identified as potential terms are considered further for the semantic class assignation problem. A potential term Tk might be classified into a term class ci that belongs to a set of classes C,


C = (c1, c2, ¼, cn).

Potential terms that are not assigned to a class in C are assigned to the class cother. Hence, the class cother changes in scope depending on the class set C. The set of all terms in a sentence is defined by a vector T, while the set of classes assigned to the terms in T is defined by a vector C.

3.2.2  Conditional random fields

Conditional random fields (CRF) [195,196] is a machine learning technique (in addition to those presented in Section 2.4.1). It is a type of graph probabilistic model that has been proved to be very suitable for sequential problems. CRF builds on Markov random fields theorems. To visualize Markov random fields, consider a lattice system (a graph) of mutually interdependent random variables. The Markov property allows us to model how a variable is influenced by all the other variables by considering only its immediate neighbors. More formally, for a set of variables C, the ith variable Ci is surrounded by a neighborhood of variables CS. The conditional probability distribution of Ci can be described as

p(Ci | C) = p(Ci | CS ),

where CS,Ci Ì C [197,198]. Exponential distributions are a set of functions that are amenable for Markov random field modeling and that minimize assumptions over the system (see Section 2.4.1). Equation 3.4 can be thus rewritten as

p(Ci | CS ) = 1


exp æ

lj ö

where Z is a normalization factor that makes the distribution sum equal to one.

In the case of CRFs, the neighbor relationships are defined by the interdependencies between features and classes. Intuitively, the class of a token 20 within a sentence is strongly related to the class of the nearby tokens. Given that a sentence easily resembles a chain graph structure, we could redefine Equation 3.5 to capture the dependencies between the tokens in a sentence and its features. A linear-chain conditional random field can be defined as


p(Ci | F ) = 1


exp æ
lk fk(cj,cj1, Fj) ö

where Z is


Z =
exp æ
lk fk(cj,cj1, Fj) ö

The index j refers to the members of the vector C, and the index k to the members of the vector F. The feature functions fk(cj,cj1, Fj) define the dependencies. Feature functions are, in our case, simple indicator functions that are equal to 1 for selected variable combinations,


fk(cj,cj1, Fj) = 1{j=k}.

Solving the linear-chain conditional random field entails estimating the parameters, q = {lk}.

Chapter 4
Biomedical term recognition and classification using large corpora and search engines

4.1  Introduction

As explained in Section 3.1.2, most work in term recognition and classification is based on the named entity recognition (NER) task from the Message Understanding Conference 6 and 7 (1995 and 1997). The task blended both term recognition and term classification for systems specialized in extraction of terms from a specific number of classes. This was a departure from early work that separated both tasks, as explained in Sections 3.1.1 and 3.1.3. The NER approach has its own problems as it offers little flexibility in the number of classes that can be recognized. For example, with this approach algorithms are specifically tuned to the features that are more likely to identify a class (e.g. suffixes for recognizing chemical terms). If term recognition and classification are performed at the same time, little leeway is left for multi-class classification other than running different term recognizers/classifiers sequentially over a text. Perhaps as a result, most work in term recognition/classification has been limited to at most five classes (notably, work presented at the Joint Workshop on Natural Language Processing in Biomedicine and its Applications [129]), and very often to single-class recognition/classification. Gaizauskas and colleagues [152] used 12 sub-cellular classes with a small training/testing set and hand-crafted rules. Other, existing proposals for more than five classes are low-performing [156,160,133].

Krauthammer and Nenadic [101] presented a framework for term identification in biomedicine that consists of three parts: (1) term recognition, (2) term classification, and (3) term mapping. A system that follows this framework and completely separates each stage could exchange parts like a layered architecture with interfaces for communication. The term recognition stage should be able to recognize all potential terms, in the spirit of the study with named entities by Black and Vasilakopolos [116] 21. Two studies have experimented with separating term recognition and classification with biomedical text [133,199], with the argument that features of importance are usually different for term recognition and term classification.

For true multi-class recognition and classification the system should have a high performance level and work well with new words without necessarily using dictionaries (as noted by Mika and Rost [141]). For term recognition, the main hurdle would be the definition of term boundaries, as they could prove to have variable properties depending on the term class. For term classification, the classifier should be flexible enough to adapt to any classes required by the task at hand, whether it is a single-class or a > 30-class problem. Machine learning classification provides higher flexibility that rule-based methods or probabilistic methods.

The following is an example of rule-based classification:

“Animals can be divided into:

      a. belonging to the Emperor
      b. embalmed
      c. trained
      d. pigs
      e. sirens
      f. fabulous
      g. stray dogs
      h. included in this classification
      i. trembling like crazy
      j. innumerable
      k. drawn with a very fine camelhair brush
      l. et cetera
      m. just broke the vase
      n. from a distance look like flies.”


We would prefer a system with more flexibility, like a machine learning approach. Additionally, we would like our system to have other characteristics that we have tried to implement:

  • The performance limits of term classification should be limited by the separability of the semantic spaces [202]. This is simply stating that we want our classifier to be as good as possible.
  • Term boundaries and classes should be defined by examples. The examples would define both the dimensions of the semantic space and of the syntactic space23. The syntactic space comprises the space that defines horizontal token relationships such as term boundaries. This is simply stating that terms are in the eye of the beholder: There are no rules set in stone for term boundaries or semantic class. Defining terms is a contingent task and therefore examples are a natural basis for a flexible approach.

4.2  Term recognition

4.2.1  Text pre-processing and indexing

In our system, text is tokenized and tagged for POS with Medpost [203], a highly accurate tagger adapted to biomedical text, with an F-measure of 97% in Medline abstracts. We performed shallow parsing for noun phrase information using YamCha, the winner of the CoNLL 2000 Shared Task, Chunking [204], which has a reported F-measure of 94% (after tagging and chunking).

Different corpora were used for statistical and context analysis. The corpora include were the BioMed Central corpus of articles, GeneWays articles, Wikipedia, the Reuters corpus, and the Medline abstracts, for a total of more than 150 million sentences. Every corpus was processed for sentence boundaries, tagged, and chunked. Sentences were indexed using the open source Apache Lucene technology from the Apache Software Foundation [205]. Although only the sentence text was indexed, additional information such as POS tags and shallow parsing was stored along with the indexed sentences for quick retrieval. Once the sentences are indexed they can be quickly retrieved performing queries using Lucene, similarly as it is done in commercial search engines like Yahoo! or Google. The two main advantages of indexing and searching for our analysis are that: (1) it allows for fast n-gram and statistical computation for phrases of any size n, and (2) it can retrieve contextual information for any text. With the large amount and breadth of sentences that we indexed, the search engine can access contextual information for many term types and variants and thus can provide a quick snapshot of term usage in real text.

4.2.2  Syntactical model

The approach used is to learn the structure of terms within noun phrases from examples in the biomedical corpus GENIA V3.02 [127,206] and then apply this structure to any noun phrase, whether or not it has a GENIA term in it. In some sense we are modeling a meta-term, or term boundary definition, that allows us to predict term boundaries regardless of class: a class-independent, example-based term boundary. We assume that what some may consider a term others may not, therefore we intend to recognize any possible term boundary and leave to classification the task of deciding what is of interest. The success of the model hangs not only on the feasibility of this proposition but also on the annotation coherence of the corpus. Annotation criteria variations could be considered noise, which must be confronted by any tagging system, but the noise may be harder to filter when more term classes are involved and the search space is broader, as it is in our case.

A limitation of the syntactical model is the number of example terms, in this case GENIA terms, as some classes have fewer number than others. Moreover, as class definitions are based on GENIA criteria, we depend on their semantic separation for good results. The validity of our approach should be tested using other corpora for cross-validation. Another limitation of our approach is that it works best for texts that are not very semantically unbalanced, which means that a significant percentage of noun phrases should contain terms. Corpora are the best source for term examples because they represent actual written term usage-rather than a selected list from an artificial dictionary. An advantage of GENIA over other biomedical corpora is that it contains more than 30 term classes.

Syntactic processing

Noun phrases produced by chunking are the starting point of the syntactic strategy. Noun phrases are syntactical phrases headed by a head noun (or pronoun) that may be accompanied by modifiers such as adjectives or other nouns. Typically, most terms in which we are interested end in the head noun of a noun phrase. For example, 81.5% of terms in the corpus GENIA end in the head of a noun phrase. Therefore, if we can identify these terms precisely the term boundary problem is greatly reduced in scope. Terms that end in a noun phrase’s head noun can be divided in 3 categories:

  • Part of a noun phrase, e.g., [ positive T cells ] (26.1% of GENIA).
  • Complete noun phrase, e.g., [ T cells ] (50.7% of GENIA).
  • Terms that overflow (4.9% of GENIA).

    These are made of more than one noun phrase and overflow the noun phrases that come after (e.g., [ University ] of [ Chicago ]). Sometimes, this situation occurs due to tagging or chunking errors that may be fixed in part as will be explained below.

In addition, many terms are within noun phrases but do not end in a head noun (14.1% of GENIA). Of these, some are nested within other GENIA terms, and others are nested in a potential term of a class that is not within GENIA (in our case, class other). The former are what more usually are called nested terms. Our model considers both of these types as nested because every noun phrase is a possible term (even if from a non-useful category). Finally, some terms do not fit in the definition above, because of chunking or tagging errors (1.6% of GENIA), and some terms can not be recognized due to problems with punctuation signs such as hyphens (e.g. IL-2 in “several IL-2-inducible DNA binding activities”) (2.8% of GENIA). Untangling these cases mixes semantics with tokenization and an increase in complexity. These latter terms that presented problems with punctuation signs or chunking/tagging errors were not considered.

4.2.3  Term recognition process

The algorithm to identify terms proceeds as follows:

  1. We select a noun phrase and identify the tokens of the noun phrase that could be part of the term.
  2. We check whether the immediately following noun phrases could be associated with the tokens identified in step 1.
  3. We identify potential nested terms among the tokens chosen in steps 1 and 2.

We implemented step 1 of the algorithm using two approaches. The first uses maximum entropy iteratively, and the second uses conditional random fields (CRF). The algorithms were trained and tested with terms from GENIA that ended in the head of a noun phrase. The iterative algorithm starts at the first token of the noun phrase and tests whether the token should belong to the term. If the answer is negative, it discards the token and continues to the next token to the right. If the next token does belong to the term then the algorithm stops and all the words to the right are considered part of the term. The MaxEnt software used is from [207]. The CRF approach is not iterative because the features of all the words of the noun phrase are considered at the same time. CRF is a hidden-state graphical model that can take into account interdependencies between features of different tokens (see Section 3.2.2 on CRFs). We use two labels for the Markov model: O for tokens that belong to the term andI for tokens that do not. There is only one state change allowed, from O to I. The CRF software is from [208].

The task can be made extremely accurate and for step-by-step MaxEnt accuracy (meaning how many left term boundaries were correctly found) was 97.7 ±0.3%, higher than for CRF. A number of new features used for this algorithm were corpus-based and they were generated using the search engine. For example, we computed the co-occurrence frequencies of token pairs and triplets, and the frequency of phrases. Perhaps the most important features, however, were the POS tag statistics. For example, phrases that are terms sometimes have a determinant before the term that is commonly not considered a part of the term. We computed frequency of POS tags for tokens and phrases using the search engine as well.

The second step of the algorithm involves joining noun phrases together. Negative examples for training/testing are pairs of consecutive noun phrases, in which only the first member of the pair has a GENIA term (non-nested). Positive examples are pairs of noun phrases that have a GENIA term that spans both noun phrases. Performance in this task using Maxent2 was 97.5 ±0.3% accurate but, the data being unbalanced, F-measure was 89.6 ±1.1%. Most training and testing examples are negative (most terms occupy only one noun phrase) and many of the positive examples are actually the product of tagging or chunking errors that divide noun phrases in two. This join step is helpful in fixing some of those chunking/tagging errors (i.e., some of the noun phrases that were split by mistake are rejoined).

The algorithm to identify nested terms is CRF based. The CRF labels were adapted to the nested term organization in GENIA. We defined the labels to be:

  • B: token beginning of term
  • I: intermediate token
  • E: token end of term
  • U: unary(one-token) term
  • UE: unary term and end of term
  • UB: unary term and beginning of term

As in other corpora, GENIA does not include interwoven terms. That is, any pair of terms that shares tokens occurs because one term is the parent of the other (see Section 3.2). Labeling examples can be seen in Table 4.2.3. If interwoven terms are not allowed, the labeling model proposed is able to capture any nesting pattern.

Term Labels Nested terms
Jak tyrosine kinases O U O tyrosine
NFATp / AP-1 complex formation B I I E O NFATp / AP-1 complex
GM-CSF receptor alpha promoter UB I E O GM-CSF, GM-CSF receptor alpha

Table 4.1: Examples of word labels for nested terms.

The CRF features used are similar to those in [209], with the addition of term and n-gram frequencies generated by the search engine. The nested term task was the hardest and the F-measure was only 64.7 ±4.2%. There are several reasons for this:

  1. It is a harder task due to multiple possible state transitions,
  2. It is unbalanced, only 11% of potential term tokens are part of nested terms,
  3. GENIA is more inconsistent in the tagging of nested terms than in the tagging of parent terms.

Nested terms are often ignored or their prediction yields low performance in other studies [133]. Overall, combining the boundary detection processes yields an estimated F-measure of 91.3%. This is only slightly lower than the estimated performance of the chunker, 94%. If we consider only potential terms that end in the head of a noun phrase, or are made of multiple noun phrases, performance is 94.7%, which shows the degree of resilience of the system to chunking errors, not only by rejoining noun phrases but also by considering nested terms that should be ending in the head of the noun phrase and they are not because of chunking/tagging errors. Perhaps surprisingly, chunking is the most important limiting factor for performance. The key fact for the system’s performance is that all noun phrases are considered, which reduces the complexity of the search space.

4.3  Term classification

4.3.1  Features

The usefulness of a feature for term classification varies widely depending on the class of interest [144,143]. Our approach builds on the features proposed to date, plus a new set of contextual features meant to describe a term’s general and local usage. Classical features that we included are (as seen in Section 3.1.3):

  • Morphological
    • Word formation patterns (e.g., whether the name has letters in upper case, digits, Greek letters, etc.)
  • Substring
    • Suffixes
    • Prefixes
  • String matching
    • Dictionary lookup of phrases and constituent words

For contextual features, we decided to use a broader model than those commonly used. We wanted to take advantage of the large available corpora to learn a term’s usage patterns to decide the class. Chang and colleagues [148] used Medline to generate a short list of signal words for the class “gene”. They defined signal words as those situated right before or after a term and that had strong class sensitivity and specificity (e.g., for the class “gene”, the words “promoter”, and “expression”), including both positive and negative signals. This represented an improved version of the classic F-term feature ([124], and see Section 3.1.4 for a description of F-terms).

Our approach has been to cast a much wider net than signal words by using all of the contextual information for all of the mentions of a potential term across corpora. As explained in Section 4.2.1, we indexed more than 150 million sentences from several biomedical and general text corpora. Sentences are a well-understood text unit and are easier to identify than paragraphs depending on the raw corpus format. Indexing sentences speeds up processing compared to indexing full documents because retrieval and location of terms take up more time when dealing with full documents. As an alternative to sentences, we also explored paragraph (abstract) indexing.

The contextual algorithm works as follows: For every term or sequence of words of interest, some indexed sentences that include the term are retrieved. Those sentences, excluding the terms themselves, are grouped to create a document that defines a semantic space. Because the documents do not necessarily include all sentences that include a term we call them snippets. Once these snippets are created for each term of a training set, we use document classification techniques [210] to classify them. The snippets from the training term examples define the semantic space of each class and the snippets of the test term examples are assigned according to how similar they are to the training set snippets. The snippets are thought of as term descriptors and the term classification is aided by using them. A new term that we would like to classify is assigned to a class according to the snippet that describes it and how similar it is to the snippets of other classes.

To create the snippets we started with the common bag of words (BOW) approach. In BOW, context is represented as a vector of ones and zeros depending on whether or not a word is present. A more effective approach is to use term frequency-inverse document frequency (TFIDF) values instead of zeros and ones. The TFIDF were computed using as background token frequencies the average frequencies of tokens across snippets of all classes (this is a crucial detail). Stop words are words that hold little discriminatory power because they are extremely frequent (e.g., the, of, a), and they were not considered in our method.

To improve classification, we found that:

  • Eliminating low frequency words improved classification. This is contrary to most (but not all) work on document classification, but filtering low frequency words seemed to reduce over-fitting, and, moreover, reduced processing time.
  • Simple stemming did not improve classification. Again, this runs contrary to previous work (but not all). Stemming biomedical words might be too complex for simple and ubiquitous stemmers like the Porter stemmer [211].
  • The more sentences the better but after a certain amount diminished returns do not compensate processing time. The more example sentences the better the classification but after about 200 sentences performance increased little. In the Lucene technology, retrieval time depends on the number of items retrieved, thus, we limited the maximum number of sentences retrieved to 200. We called these short documents of 200 sentences or less “snippets”. Overall, this contextual technique may be called snippet classification.

This method of semantic classification is conceptually similar to one of the tools used by human curators of dictionaries called Key Word In Context (KWIC)24 . In KWIC, the curators are presented with a list of examples of a word use and they decide the class according to those examples.

To improve performance we also implemented contextual weighting. Intuitively, words that are nearer to a term are more related to that term than words that are farther away. Heuristic contextual weighting measures have been devised before (for example, see [59]). A more elaborate method is to model the contextual weighting following a decay function. [171,212] developed an analytical model of contextual weighing using an exponential decay function. The parameters of the function were determined iteratively by search. An important finding was that optimal parameters depended on the type of text [212] and are not universal. We improved results in our system by applying a weighting with an exponential function with a decay of 0.1 using a window of 20 tokens around the term. Tokens farther apart received a weight of 0.

4.3.2  Local, regional, global: Word sense disambiguation

As explained in Section 3.1.4, word sense disambiguation (WSD) can be considered a problem subclass in term classification. Rather than performing sense disambiguation, our approach has been to generate different features for classification based on context. Classifying terms as explained in the previous section brings about a global semantic classification-an abstracted classification that disregards the specific sense in which a term is used in a text (in the spirit of [213]). However, this is a problem for terms with more than one meaning, such as terms that are both the name of a gene and a protein. Biomedical terms have lower persistence of sense than other term classes (perhaps as low as 60% [59,132], see Section 3.1.4 for further discussion on WSD).

Our solution to the problem has been to divide the semantic space into three spheres:

  • Local: term class in the text where a term is used. This involves contextual weighting of sentence words, as explained above.
  • Regional: term class within a knowledge pocket. This entails emphasizing sentences from the same document or from documents cited. Citations were parsed from the GeneWays corpus and mapped to Medline IDs. Citation is a shortcut for relatedness. Other document similarity measures might work too.
  • Global: term class across corpora. We use snippets of up to 200 sentence examples, as explained.

All of these features mentioned were combined into a single vector for maximum entropy classification. While SVM is the technique of choice in entity classification, maximum entropy showed slightly better performance, and faster training especially in multiclass assignment. This yielded the best results reported separating 10 GENIA classes (compare to state-of-the-art in Table 4.3.2). Classification results did not diminish with the addition of more classes. The system is able to classify the original 10 classes equally well even if more classes are added. We have not established what is the limit of this property, but it is certainly an invariance that is desirable. The F-measure for 33 classes was 80.4 ±0.8% and 89.4 ±1.1% for 10 classes. There is a correlation between the number of training samples and accuracy, suggesting that results can be improved in classification for any class if the number of examples increases.

The conceptual model of separation into three spheres local/regional/global tries to capture actual term use. Rather than one-sense-per-discourse approach (see Section 3.1.4) we hypothesize that term class is heavily influenced by the subject that the text addresses. A financial report is most likely to use the word bank to refer to a financial institution than to refer to a riverbank. A discourse dealing with different subjects may use the word bank in different ways. This is rather an important change in interpretation because if we were interested in the meaning of the word bank in a financial document we could find help reading other financial documents-documents that deal with the same subject.

Class F-measure (this study) F-measure [214]
Protein 94% 91%
DNA 94% 85%
Cell type 82% 84%
Other organic compound 85% 70%
Cell line 81% 65%
Multi cell 96% 85%
Lipid 76% 87%
Virus 88% 88%
Cell component 96% 84%
RNA 90% 77%
Total 90% 86%

Table 4.2: Term classification performance.

Chapter 5
Six senses: the bleak sensory landscape of biomedical texts

It is beyond our power to fathom,
Which way the word we utter resonates.
Fedor Tyitchev

We analyzed the frequencies of use of sensory words (describing touch, smell, sight, taste, and sound) and time-related terms in a very large collection of biomedical texts. We then compared the results with similar analyses of a collection of news articles, a large encyclopedia, and a body of literary prose and poetry. We found that, unlike literary compositions and newswire articles, biomedical texts are extremely sensory-poor, but rich in overall vocabulary. It is likely that the sensory-deprived writing style that dominates the biomedical literature impedes text comprehension and numbs the reader’s senses.

When we read technical or literary prose, chains of words flowing through our minds invoke sensory responses that can be surprising (unexpected) even for the writer. Even a very technical text typically affects the reader on multiple levels, in addition to transmitting the author-intended content.

Prose can profoundly alter the physiological and emotional states of an unsuspecting reader. The semantic priming test in modern psychology exploits this phenomenon. For example, people start feeling and behaving as if they have suddenly grown older after reading a scrambled sequence of words enriched with aging-related connotations [215]. The priming effect is largely independent of our conscious understanding of a text: autistic children whose text comprehension is mildly impaired respond to semantic priming similarly to non-autistic kids [216]. Furthermore, our emotional response to a sequence of words depends on our genetic background: for example, children of parents with bipolar disorder react much more vividly to words that have undertones of a social threat than do children in a control group [217]. Semantic priming can profoundly affect the model of the outside world reported by our senses: merely naming an odor (“cheddar cheese” vs. “body odor”) can determine our perception of the odor as pleasant or nauseating [218].

The selection of words in a composition also reveals deep personality traits of its author to the reader. For example, a person’s color preferences and idiosyncrasies provide information relevant to her psychological evaluation [219]. Schizophrenia patients-who are especially susceptible to semantic priming-have a characteristic utterance pattern: the patients’ own words generate diverse secondary associations in their minds. These self-inflicted associations surface in the patients’ utterances and disturb the clarity of their messages [220].

Computational analysis of scientific language typically serves as the groundwork for engineering text-mining tools [221,222]. This analysis also provides us with a unique glimpse into the “collective unconsciousness” of a scientific community. In this study we compare the frequencies of sensory terms, such as those related to the perception of color, smell, taste, touch, sound, and time, across multiple large corpora. We use this comparison to infer a “collective sensory landscape” of the biomedical literature and the hypothetical priming that biomedical texts exert on their readers.

We analyzed a large collection of scientific texts (Journals, including almost 250,000 full-text articles) representing 78 biomedical journals. We compared the properties of biomedical texts with those of news reports (Reuters), the open-access encyclopedia Wikipedia (Wiki), and complete collections of the compositions of Edgar Allan Poe (Poe), William Shakespeare (Shakespeare), and Walt Whitman (Whitman). We grouped these corpora into those that are collective (Journals, Reuters, and Wiki) and those that are individual (Poe, Shakespeare, and Whitman).

When discussing time (Figure 5.1 A), all six corpora most frequently mention days and years. In individual corpora, days predominate over all other time terms, followed by hours and years. In collectivecorpora, years are most often mentioned, followed by days and seconds. While individual corpora remain exclusively within the second-to-century range, collective corpora reach into picoseconds on the short-term side, and into millennia (and even millions of years, not shown) on the long-term side of the range of time-scales. Within individual corpora, Whitman is the most concerned with centuries, and Shakespeare the least. Reuters is almost twice as time-obsessed as Whitman; all the other corpora are several-fold poorer in time-conscious words (see Figure 5.1 E). Biomedical texts are among the poorest in time-related terms, although Wiki and Shakespeare are even poorer.

Figure 5.1: Analysis of the frequencies of sensory words in six large corpora: Journals, Wiki, Reuters, Shakespeare, Whitman, and Poe. A. Frequencies of time-related terms. B. Share of sensory terms divided into five sense groups. C. Frequencies of color-related terms grouped into a Berlin-Kay-like taxonomy (inset) computed for six large text corpora. D. Balance of sensory terms in different corpora compared to the average (face in the center). E. Combined frequencies of time-related terms. F. Multidimensional scaling of time-related word frequencies. G. Combined frequencies of five-sense-related terms. H. Multidimensional scaling of five-sense-related word share. Journals (GeneWays 6.0) is a collection of nearly 250,000 full-text articles from 78 research journals (see [63] for the complete list of journals). Wiki (Wikipedia) is an open-access encyclopedia that rivals Encyclopedia Britannica in accuracy and far surpasses Britannica in breadth and coverage. Reuters (Reuters Newswire 2000) is a corpus of news articles in multiple languages (we used only the articles in English). Shakespeare (William Shakespeare, 1564-1616) is a complete collection of the works of probably the best known English writer and poet. Whitman (Walt Whitman, 1819-1892,) is a corpus of compositions of probably the most famous American poet. Poe (Edgar Allan Poe, 1809-1849) is a complete collection of works by the prolific and influential writer and poet, whose life was short and tragic.

When we consider words related to the five basic human senses (Figures 5.1 B and G), sight-related terms are most frequent in all six corpora (Figure 5.1 B). The collective prose is significantly more visual than the individual, but the trend is reversed for taste, smell, touch, and sound-related terms. Among the individual corpora Shakespeare is the least visual, but the richest in taste, smell, and touch-related terms, when sensory word frequencies are normalized to sum to 1 within each corpus. However, if we look at the absolute frequencies of sensory terms, the differences between corpora are staggering (Figure 5.1 G): Whitman overall is the richest in sensory terms, closely followed by Reuters. Poe, the next in ranking, reaches barely half the frequency of sensory-related terms found inReuters and Whitman. Compared to Whitman and Reuters, sensory terms are nearly absent from Journals, Shakespeare, and Wiki, with Wiki being the most sensory deprived. The balance between different sensory terms (combined with the overall dictionary size) is visually highlighted in Figure 5.1 D: individual corpora have, understandably, more limited vocabularies and are significantly richer in non-visual sensory terms, but poorer in visual sensory terms, than collective corpora. (Note that these differences in vocabulary richness, as well as all other properties in Figure 5.1 D, are logarithmic rather than linear in scale.)

To highlight the similarities and dissimilarities in the frequencies of the numerous sensory terms among the six corpora, we used a multidimensional scaling technique [223] (see Figures 5.1 F and H). Multidimensional scaling at its heart is a task of reconstructing a geographic map from a set of known distances between cities: in our case, we are trying to arrange points corresponding to our six corpora on a plane so that the resulting distances are as close as possible to the Euclidean distances between corpus-specific vectors of frequencies of sensory terms. Both in the case of the time-related (Figure 5.1 F) and the five-sense-related (Figure 5.1 H) terms, the collective and individual corpora form two distinct groups. Shakespeare seems to be an outlier in both cases, while Poe andWhitman are rather similar among the individual corpora. Among the collective corpora, Journals and Wiki are the most dissimilar, with Reuters occupying an intermediate position.

There are surprising and highly significant differences in the usage of color-related terms among our six corpora-“our wits are so diversely colored” [224]. We grouped color terms according to the taxonomy proposed by the anthropologists Berlin and Kay [225,226]. The Berlin-Kay taxonomy describes a hypothetical historical origin and diversification of color terms summarized over 19 distinct cultures (see inset in Figure 5.1 C). The authors suggest that color description is rather universal across cultures, due to the universality of the anatomy and physiology of human vision. Very briefly, according to their theory, as language develops in a typical culture, description of color goes through several stages of complexity. The first stage involves just two color terms, such as warm and cool, followed by the isolation of pure white and pure black “colors”. At the later stages of color term differentiation, so-called rellow color splits into red and yellow, while so-called grue splits into green andblue.

Journals and Reuters are nearly tied in the contest for the title of the visually bleakest corpus. Reuters, the bleakest corpus (color-wise, but not in all sensory terms), is significantly biased towards warmcolors, while in Journals the frequencies of various color-related terms are nearly uniform.

In all corpora but Poe, warm colors dominate over cold colors. In Poe, not only do the cold colors prevail, but also black “color” dominates over all other colors at an extremely high level of significance. Edgar Allan Poe’s prose and poetry is literally dark. In Shakespeare’s writing the significantly dominant color is red-Shakespeare’s prose is probably tinted by action in which blood is often spilled.

Unlike Poe and Shakespeare, Whitman produced texts with color term frequencies nearly perfectly evenly distributed among the six major categories (white, black, red, yellow, green, and blue; the frequency differences are not statistically significant). This is particularly curious in light of the observation that Whitman’s writing overall is twice as rich in color terms as that of Poe and Shakespeareand almost five times as rich as biomedical prose (Journals).

What is the likely priming effect of biomedical texts on readers? Our conjecture (which can be tested rigorously by experimental psychologists) is that the priming effect is similar to the effect of a long journey through colorless flat terrain devoid of prominent features-a numbing of the senses.

We suggest that apparently cognitively bleak biomedical texts can and should be transformed into perceptually richer prose (we are not implying that it is an easy task!). Why is this important? Because the mapping of abstract concepts to objects with meaningful sensory properties serves as a stepping-stone to the solution of complex problems. Consider the following quote from Richard Feynman.

I had a scheme, which I still use today when somebody is explaining something that I’m trying to understand: I keep making up examples. For instance, the mathematicians would come in with a terrific theorem, and they’re all excited. As they’re telling me the conditions of the theorem, I construct something which fits all the conditions. You know, you have a set (one ball)-disjoint (two balls). Then the ball turns colors, grow hairs, or whatever, in my head as they put more conditions on. Finally they state the theorem, which is some dumb thing about the ball which isn’t true for my hairy green ball thing, so I say “False!”

If it’s true, they get all excited, and I let them go on for a while. Then I point out my counterexample.

“Oh. We forgot to tell you that it’s Class 2 Hausdorff homomorphic.”

“Well, then,” I say, “It’s trivial! It’s trivial!” By that time I know which way it goes, even though I don’t know what Hausdorff homomorphic means. [227]

(Credit for isolating this quote is due to Daniel Dennett [228].)

Our brain was shaped by a chain of evolutionary adaptations, each invoked by an acute necessity to address a concrete survival problem posed by our changing environment. Our neural system is therefore an eclectic ensemble of disparate pieces of hardware, perfected for solving specialized problems-such as the detection of potentially threatening bilateral vertical symmetry (a lurking predator) in the chaotic environment, or prompt recognition of the faces of the numerous members of our own tribe. To make more efficient use of our neural machinery, we need to translate abstract problems into concrete sensory-grounded symbols that can be efficiently processed by our brains. (This is like trying to do a general computation using graphics-oriented hardware: to make the computation efficient, we have to translate our task into spatial translations of three-dimensional primitives.)

When we read and compose sensory-deprived prose, we probably leave a large portion of our nervous system uninvolved-different words and meanings are processed by distinct brain areas [229]. We conjecture that a piece of sensory-poor prose does, on average, a poorer job of engaging the reader’s imagination than a sensory-rich one, although the former can be much more precise and concise than the latter. Within a narrow scientific subfield, an expert would undoubtedly prefer to read a concise technical text rather than a longer one replete with metaphors and analogies. However, the situation is different for a scientist trying to read a paper from a neighboring subfield: a dry technical description may require a prohibitive amount of a non-expert’s time to read and grasp. It is in the writer’s best interest to ensure that her work is as widely accessible as possible.

We believe that scientific prose should be enriched with sensory words (provided that they clarify the meaning rather than obscure it), in much the same way as a good statistical data visualization involves the mapping of abstract data into colors and three-dimensional shapes, thus aiding the discovery of patterns.

Chapter 6
A recipe for high impact

Every research article has at least two important ingredients: it attacks a scientific problem (topic), and invents or recycles a study technique (method). Here we quantify the relative contribution of these two elements to an article’s success by sifting through myriads of time-stamped scientific texts, accumulated over decades in the permafrost of reference databases [21].

We define and analyze here three attributes associated with each scientific article: `topic’, `method’ and `impact’. Nearly every article referenced in the PubMed database has a list of keywords reflecting its content: chosen from more than 20,000 MeSH terms and more than 150,000 chemical names [230]. We use MeSH terms and chemical names as indicators of an article’s topic and method, respectively. The `impact factor’ (IF) of the journal where the article was published is provided by the Thomson ISI database [231].

6.1  Ingredients of a scholarly study

For millions of articles published in 1,757 journals we compute two parameters (separately for topic and method concepts): `temperature’ and `novelty’, as introduced in our earlier work [63], using a reference corpus of publications pre-dating each article (see Additional data file 1). When all journal-specific articles are considered together, a high temperature of a journal indicates its tendency to publish popular (hot) concepts. The novelty parameter can change between 0 and 1, and, as the name implies, reflects the proportion of new (previously unpublished) concepts in a group of texts.

We used a five-parameter linear regression model to assess contributions of topic- and method-specific estimates of temperature and novelty to a journal’s IF (see Additional data file 1). We observe that high IFs correlate strongly with hotter topics and colder methods (see Figure 6.1 a,b). Disturbingly, both method and topic novelty are unimportant for predicting IF. Despite a strong positive correlation between the popularity of article’s topic and method-contributed by the bulk of the moderately influential articles (see Figure 6.1 b, inset)-the highest-impact scientific research emerges when very popular (important) topics are tackled with unpopular methods.

Figure 6.1: Contributions of topic- and method-specific estimates of temperature and novelty to a journal’s impact factor. (a) Relationship among the method-temperature (chemical), topic-temperature (MeSH), and the impact factor of 1,757 journals. (b) Volume (number of mentions) distribution of topics and methods. Inset: significant (p < 0.01) correlations between pairs of the five parameters. Green and red lines indicate positive and negative correlations, respectively, with line width proportional to the corresponding correlation strength. (c,d) Estimates of temperature and novelty parameters for various publication types with 95% credible intervals. Ovals indicate closely grouped estimates; labels are listed in decreasing novelty.

Our topic and method terms have very different frequency distributions-reflecting the difference in their genesis. In the former case, it is a human expert who decides that a new concept is sufficiently frequently used to merit its addition to the controlled MeSH vocabulary. In the latter case, the list of new terms is not artificially restricted; they are allowed to be very rare (see Figure 6.1 b). As a result, frequencies of the chemical terms follow a classical Zipf’s distribution, while MeSH terms clearly deviate from this distribution due to deficiency of the rare terms (see Figure 6.1 b).

6.2  Information flow through publication-type niches

Figure 6.1 c,d illustrates the unique (statistically distinct) niches of distinct publication types in the space of novelty and temperature. For methods (chemicals, including drugs), information diffuses from novel-unpopular to known-popular publication types. `Colder’ chemicals are published first in the journal articles; some of them later make it to the warmer and less novel space of phase I clinical trials, and a subset of these drugs makes it to the significantly warmer area of phase II clinical trials (Figure 6.1 c). Furthermore, the growth of temperature and loss of novelty progressively accelerates to reviews, lectures and biographies. Curiously, the retracted and corrected papers (Figure 6.1 c), along with news, are champions in the novelty competition-it looks almost as if the retracted articles are too novel to be correct. For topics, we observe a similar-albeit less intuitive-picture (Figure 6.1 d), where retracted articles again have the highest novelty. The clinical trial story shows a new twist here: most clinical trials take years; they persist long enough for their initially hot topics (at the stage of a research article and phase I clinical trial) to cool down before reaching phase II and III trials (Figure6.1 d)-a consequence of the time-dependence of temperature estimates that capture ephemeral fads within biological disciplines.

Our analysis highlights the importance of choice of a research topic, and of putting new work in the right context. A remarkable idea (method) presented to the world in a wrong context (topic) has little chance of being noticed. A successful idea travels through publication types much as energy flows through an ecosystem: it is typically born novel and unpopular in research articles (plants), and diffuses eventually to reviews, lectures, clinical trials, and bibliographies (top-hierarchy carnivores), where it reaches the pinnacle of popularity.

6.3  Additional information

6.3.1  Data

We parsed 8,592,483 PubMed records, extracting from each the PubMed ID, journal name, year of publication, publication type, chemical names, and MeSH terms. We chose a time-span between 1985 and 2004 characterized by a steady growth of the number of MeSH terms, chemical names, and articles (Figure 6.2). Our dataset for this period covers 12,039 journals that mention a total of 22,371 unique MeSH terms and 153,756 unique chemical names. There are 49 different publication types.

Figure 6.2: Number of articles, MeSH terms and chemical names mentioned in PubMed since 1950.

6.3.2  Analysis

A list of terms (MeSH terms or chemical names) that accompanies most time-stamped records in the PubMed database allows us to monitor how popularity of terms changes in time. Given a time point t, we define Nti as the number of terms that occur in PubMed before t exactly i times. To characterize the probability of encountering terms with the same level of popularity (number of instances in PubMed before the same point of time), we introduce a popularity variable q that takes integer values 0, 1, 2, ¼; notation P(q | t, parameter values) represents the expected proportion of terms with popularity q at time point t given our model and parameter values.

When we model stochastic generation of scientific texts, we assume that each time-stamped text is allowed to contain terms with zero popularity (novel terms), and that the expected frequency of such terms is b, a parameter that we call novelty:

p(q=0 | Nt,a,b) = b,

where Nt is a vector summarizing all popularity counts associated with time point t. We further assume that the expected frequency of known (q > 0) terms is

p(q | Nt, a, b) = (1 b) Ntqqa


where a is another model parameter that we call temperature.

We use Equations 6.1 and 6.2 to compute the likelihood of any collection of term mentions given parameter values, and, assuming an uninformative prior parameter distribution, estimate the joint posterior distribution of a and b.

In our analysis, we first estimate the novelty and temperature separately for topic (MeSH) and method (chemical) content of articles published in journals that mentioned at least 1,000 MeSH terms, and at least 1,000 chemicals within the chosen interval, and had a known impact factor. This left us with a set of 1,757 journals. The journal’s impact factor was computed as an average of its IF values reported between 1999 and 2004.

We use the following linear regression model with a stepwise regression analysis framework to test for a five-way correlation among journal specific parameters ( a and b are temperature and novelty, respectively) and the impact factor (IF) of a journal,


IFi = A atopic,i + B btopic,i + C amethod,i + D bmethod,i + E + error,

where subscript i refers to the ith journal and A, B, C, D and E are parameters of the linear regression model. We assume that the error term follows a normal distribution. Our analysis shows that estimates of B and D are not significantly different from zero. The estimate for A is significantly larger than zero (4.55, with 95% confidence interval [3, 6]) and estimate for C is significantly smaller than zero (-9.8, with 95% confidence interval [-12.6, -7]).

We estimate model parameters and credible intervals for publication types using a version of the Markov chain Monte Carlo approach (see Figure 6.1 c and 1d; we use the maximum posterior probability estimator in each case). Parameter estimation for topics is done for publication types that mentioned at least 1,000 MeSH terms (same selection strategy applies to parameter estimation for methods). `Average temperature’ and `average novelty’ refers to a weighted average of temperature and novelty when all publication types are considered together.

To fit the topic and method volumes to a Zipf’s (Pareto) distribution we use the maximum likelihood estimate of g-parameter of Zipf’s distribution; our estimates of g-values for topic and method volumes are 1.153 and 1.528, respectively.

Chapter 7
How many scientific papers should be retracted?

7.1  Analyzing retraction patterns

Published scholarly articles commonly contain imperfections: punctuation errors, imprecise wording and occasionally more substantial flaws in scientific methodology, such as mistakes in experimental design, execution errors and even misconduct [232]. These imperfections are similar to manufacturing defects in man-made machines: most are not dangerous but a small minority have the potential to cause a disaster [233,234]. Retracting a published scientific article is the academic counterpart of recalling a flawed industrial product [235].

However, not all articles that should be retracted are retracted. This is because the quality of a scientific article depends, among other things, on the effort and time invested in quality control. Mechanical micro-fractures in turbojet components are detected more readily than those in sculptures, as airplane parts are typically subjected to much more rigorous testing for mechanical integrity. Similarly, articles published in more prominent scientific journals receive increased attention and a concomitant increase in the level of scrutiny. This therefore raises the question of how many articles would have to be retracted if the highest standards of screening were universally applied to all journals.

PubMed provides us with a `paleontological’ record of articles published in 4,348 journals with a known impact factor (IF). Of the 9,398,715 articles published between 1950 and 2004, 596 were retracted. This wave of retraction hits high-impact journals significantly harder than lower-impact journals (Figure 7.1 A), suggesting that high-impact journals are either more prone to publishing flawed manuscripts or scrutinized much more rigorously than low-impact journals.

Figure 7.1: Dataset, model and estimation of the number of flawed articles in scientific literature. (A) Higher- and lower-impact factor (IF) journals have significantly more than expected and less than expected retracted articles, respectively. Each sphere represents the set of articles within the same IF range; the volume of the sphere is proportional to the set size and its color represents the middle-of-the-bin IF value. The expected number of retractions is calculated under the assumption that all retractions are uniformly distributed among articles and journals. The red line indicates a hypothetical ideal correlation between the observed and the expected numbers of retractions. (B) and (C) explain the four-parameter graphical model describing our hypothetical stochastic publication–retraction process. (B) Estimated posterior distribution of parameter values for several values of impact-independent scrutiny. (C) Outline of the stochastic graphical model (top) and the posterior mean estimates of the number of articles that should be retracted (with 95% credible interval) plotted against different values of IF. Posterior distributions of estimated number of retractable articles: red- and green-colored distributions correspond to t = 0.1 and t = 1, respectively; horizontal black solid lines indicate the actual number of retracted articles for individual IF bins and journals. The contour distributions represent individual journals, whereas the solid distributions correspond to the whole PubMed corpus binned by the IF value.

Here, we introduce a four-parameter stochastic model of the publication process (Fig 7.1 C), which allows us to investigate both possibilities (see Supplementary Information for full details). The model describes the two sides of the publication–retraction process: the rigor of a journal in accepting a smaller fraction of flawed manuscripts (quality parameters a and q), and post-publication scrutiny on the part of the scientific community (scrutiny parameters b and t). We need four parameters to account for the possibility of both IF-independent and IF-dependent changes in quality and scrutiny (see Fig7.1 C, top). For each journal we compute a normalized impact r (a journal-specific IF divided by the maximum IF value in our collection of journals), and use it to define the strength of IF-dependent quality (ralpha) and IF-dependent scrutiny (rbeta).

This probabilistic description allows us to distinguish between the two scenarios described above for the higher incidence of retractions in high-impact journals. For all values of t tested, we find that the posterior mode of the IF-dependent quality parameter (a) is close to 0 (Figure 7.1 B), indicating a nearly uniform and IF-independent rigor in pre-publication quality control. Conversely, the posterior mode of the IF-dependent scrutiny parameter (b) is essentially 1 (Figure 7.1 B), which translates into a linear dependence between the IF of the journal and the rigor of post-publication scrutiny. Our data therefore suggest that high-impact journals are similar to their lower-impact peers in pre-publication scrutiny, but are much more meticulously tested after publication.

Our model also allows us to estimate the number of papers that should have been retracted under all plausible explanations of reality permitted by the model (Figure 7.1 C). We estimate the number of articles published between 1950 and 2004 that ought to be retracted to be more than 100,000 under the more pessimistic scenario (t = 0.1; red, Figure 7.1 C), and greater than 10,000 under the most optimistic scenario (t = 1; green, Figure 7.1 C). The gap between retractable and retracted papers is much wider for the lower-impact journals (Figure 7.1 C). For example, for Nature (1999 –- 2004 average IF = 29.5), with t = 1 (optimistic), we estimate that 45–67 articles should have been retracted, whereas only 30 actually were retracted. Science (IF = 26.7) and Biochemistry (IF = 4.1) have nearly identical numbers of papers published, but the actual numbers of retracted papers for these two journals are 45 and 5 respectively.

Our analysis indicates that although high-impact journals tend to have fewer undetected flawed articles than their lower-impact peers, even the most vigilant journals potentially host papers that should be retracted. However, the positive relationship between visibility of research and post-publication scrutiny suggests that the technical and sociological progress in information dissemination—the internet, omnipresent electronic publishing and the open access initiative—inadvertently improves the self-correction of science by making scientific publications more visible and accessible.

7.2  Mathematical model to calculate the number of articles that should have been retracted

We analyzed the PubMed database, looking for retracted articles published between 1950 and 2004 in 4,348 journals with known impact factors (IFs) over a number of years. Here we compute each journal’s impact factor as an average of its IF values reported for the years 1999 to 2004 by ISI Thomson, Inc. Note that for some journals, ISI provides different IF values in the bar charts and text on their website; we have used the values described in the text.

Let IFi be the impact factor of the ith journal, and IFmax be the highest impact factor that we observe in our dataset (50.551). We define a normalized IF for the ith journal, ri, as ri = [(IFi)/(IFmax)]. Let aibe the total number of articles published in the ith journal and yi be the number of retracted articles in the same journal. According to our model outlined in Figure 7.1 C (inset), the probability of observing retraction of yi out of ai articles published in the ith journal, computed jointly for all N journals in our dataset (i=1, ¼,n), is


p( {ai,yi,IFi} | Q) = æ

a1 ¼an

rbi ]yi [1(1qrai )
rbi ]( ai yi ) ü

In this expression p(IFi) is the probability of sampling an article that is published in the ith journal (with impact factor IFi). Note that the multinomial probability of sampling the whole observed article set, (

åi ai
a1 ¼an

)Õi=1n p(IFi)ai, and the binomial coefficient, (


), do not depend on the values of our model parameters and, therefore, they can be omitted in the maximum likelihood and MCMC computations.

A set of 5 journals from the ISI dataset have IF assigned to 0. Most certainly the articles published in these journals are cited somewhere, but these citations fall outside of the set of journals reviewed by the ISI. We attempt to account for this incompleteness of the ISI data in our calculation of impact factors in the following way. For the set of 5 journals with ISI-assigned IF of 0, we postulate a pseudo-IF of 0.0009, one tenth of the smallest IF that we observe in our dataset.

In our model, parameters q and t can vary between 0 and 1, while a and b can take any real value. We require that the joint probability of q, a, t, and b be 0 whenever qrai or trbi are smaller than 0 or larger than 1, because we define these quantities as probabilities.

To estimate the posterior distribution of parameter values (given an uninformative prior distribution over parameter values), we use Markov chain Monte Carlo (MCMC; [236]). We repeat our parameter estimation while fixing t to several values between 0 and 1, using MCMC with 10 million iterations. The results of this parameter estimation are given in Figure 7.1 B.

The expected number of retractable articles for particular values of a and q can be computed as:


Ri = ai (1qrai ),


R = N

where R is the total (unobserved) number of retractable articles, Ri is the ith journal’s share of this number, and n is the total number of journals. The parameters ai and ri denote the number of articles and the normalized IF of the ith journal, respectively. We use the joint posterior distribution of a, b, and q for t = 0.1 and ƒÑ=1 values to compute the posterior distributions of R and Ri shown in Figure7.1 C.

7.3  Retraction rates are on the rise

Like Shakespeare’s plays, scientific enterprise covers the whole spectrum of human behavior ranging from genius, passion, and jealousy to mistakes and misconduct. Although we are excited about advancements in science, our reaction to mistakes and misconduct, and to the accompanying article retractions reflects the collapse of a profound belief in the truth-seeking nature of the ideal scientist, who is devoid of ordinary human flaws.

Recently, there have been a number of high-profile retractions in well regarded journals which triggers a feeling that integrity of science is in decline. Are retraction rates for scientific articles higher than in the past? Here, we demonstrate that this is indeed the case.

We searched the Medline database to calculate the number of published articles and the number of retracted articles since 1950. Our analysis indicates that more than 17 million articles have been recorded in Medline, and 871 of these have been retracted as of 21 October 2007. Not surprisingly, the number of articles published in biomedical sciences each year has been constantly increasing (Figure 1, black line). We divide the number of retracted articles by the number of published articles each year to find the percentage of articles retracted (Figure 7.2, red line). Figure 7.2 exhibits the first cases of retractions in the 1970’s, which raised awareness of the issue and triggered the establishment of the Office of Research Integrity (ORI). Interestingly, we find that the rate of retracted articles has increased in time. The low retraction rates in the first few decades of the studied period may stem from the possibility that Medline had not flagged retracted articles at that time. However, even limiting our analysis to the period between 1990 and 2006, we find a significant increase with r = 0.55 (pvalue = 0.02). It must be noted that this figure underestimates the retraction rates in recent years as there has not been sufficient time to identify the flawed articles that were published in recent years (see the sharp decline in retraction rates for 2007). Hence, if we were to analyze in future the retraction rate after the numbers of retractions are saturated for the studied time period, we would expect to see an even sharper increase in the rate of retracted articles. From these observations, we conclude that retraction rates have been increasing.

This conclusion, of course, can have two interpretations, each with very different implications for the state of science. The first interpretation implies that the ever increasing competition in science and pressure to publish pushes scientists to produce flawed manuscripts at a higher rate, which means scientific integrity is in decline. The second interpretation is more positive, suggesting that flawed manuscripts are identified more successfully, which means self-correction of science is improving.

In Chapter 7.1, we have shown that articles published in high-impact and highly visible journals receive significantly greater scrutiny, and consequently there is a higher chance for flawed articles to be identified in these journals. This study suggested that self-correction in science will improve as continuing progress in the dissemination of information (such as internet and electronic publishing) further increases the visibility of research results. An increase of flawed manuscripts may still be in effect, and this remains to be proved or disproved by further analysis. However, with scientific knowledge becoming ever more visible each day, we may anticipate that flawed manuscripts are more readily identified and self-correction in science is improving.

Figure 7.2: Number of articles and the percentage of articles retracted since 1950 as recorded in Medline.

Chapter 8
Future work and conclusions

8.1  Future work

The work reported in Chapters 1 and 2 could be applied effectively to a more standardized training/testing corpus. The automatic curation was adapted to the GeneWays system, in principle, no reason to believe that it would not work in other training/testing sets, but it certainly would have a sounder grounding if it were replicated with a third-party dataset. The Collado-Vives laboratory [237,238] already has done so using data from the RegulonDB database of transcriptional regulation in Escherichia coli. Further applications for automatic curation would have their niche specifically in text-mining applications of low performance (that is, those confronting a difficult mining problem).

Chapters 3 and 4 are different insofar as there already is extensive literature on the topic considered. Nonetheless, the innovations in the approach and features presented in these chapters could be easily applied to other fields-biomedical and non-biomedical alike (e.g., web mining). A natural application would be to use commercial web search engine results instead of our own processed corpora. The ultimate objective would still be to hit the limits of the semantic space, or, more conservatively, to approach and surpass human performance.

Chapter 5 introduced a new way to analyze the biomedical literature: its sensorial impact over the reader. It is not hard to envision other measures of the impact that the biomedical literature may have on its readers, such as emotional response, engagement, and communicativeness. These measures could point to the impact and consequences of the reading experience on the scientist. Chapter 6represents another journey to uncharted territory: finding ways for scientists to improve the impact of their work. This study was limited to analysis of two parameters but models with more parameters could be designed to capture more nuanced trends.

Chapter 7 is unique because it describes the first study designed to explore the retraction phenomenon from an analytical point of view. Previous work was based on empirical exploration and description of retracted articles. We believe that only analytical approaches can answer questions like how retraction rates will change in the future and how ingrained misconduct is in the scientific world. For example, is retraction a mere phenomenon of bad apples or a systemic behavior contained only by the watchdogs in place? Are the watchdogs effective? Another example is the analysis of the retraction cycle, from submission to peer-review to publication to discovery to retraction. Are there any hints that would allow us to be suspicious of a publication? These and other questions are hot issue still open for analysis.

8.2  Conclusion

Text mining of the biomedical literature remains an open avenue for innovation both in areas that seem mature and in areas that have yet to be thought of. The flexibility of analyzing written language with more powerful resources and larger, better repositories can only increase the quality of future work in this field.

8.3  Papers that resulted from the work in this thesis

  1. Rodriguez-Esteban R, Rzhetsky A. Biomedical term recognition and classification using search engines. (in preparation)
  2. Cokol M, Ozbay F, Rodriguez-Esteban R. Retraction rates are on the rise. EMBO Rep. (in press)
  3. Rodriguez-Esteban R, Rzhetsky A. Six senses: the bleak sensory landscape of biomedical texts. EMBO Rep. (in press)
  4. Cokol M, Rodriguez-Esteban R, Rzhetsky A. A recipe for high impact. Genome Biol. 2007 May 10;8(5):406
  5. Cokol M, Iossifov I, Rodriguez-Esteban R, Rzhetsky A. How many scientific papers should be retracted? EMBO Rep. 2007 May;8(5):422-3.
  6. Rodriguez-Esteban R, Iossifov I, Rzhetsky A. Imitating manual curation of text-mined facts in biomedicine. PLoS Comput Biol. 2006 Sep 8;2(9)


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1Think of the first cars having the shape of horse carts, or the first films looking like theater plays

2Clearly, not all those bytes are useful. The problem is not confined to sorting large amounts of data but also to seeing through the “information pollution” that clouds data analysis. It may be worth quoting T. S. Eliot again:

“Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”

3The current version of GeneWays database contains 4,035,759 redundant interactions (2,652,916 of them are unique) that involve 1,299,146 unique substance terms (with 17,903,358 redundant terms identified in total) from 232,265 full-text articles representing 78 major research journals. The spectrum of relations represented in the database is shown in Figures 2.2 and 2.3.

4We also computed the k-score for the inter-annotator agreement in the following way.

k = P(A)P(E)



where P(A)=0.92 is the observed pair-wise agreement between annotators, P(E) is the expected agreement under the random model (so long as we have a binary classification task, we assumed P(E)=[1/2]), which gives k = 0.84 for the high-agreement chunks and k = 0.48 for the low-agreement chunk, see [75] for guidelines on usage and interpretation of k-values. If we use a more sophisticated random model, accounting for the observation that in our study an average evaluator assigned the label correct with probability 0.65 rather than 0.5, we obtain P(E) = 0.652 + 0.352 = 0.545, which leads to slightly lower k-estimates of 0.82 and 0.43, for the high- and low-agreement chunks, respectively.

5The low-agreement chunk was produced by only two evaluators. We interpreted the low agreement as an indication that the evaluators, while working on this chunk, were less careful than usual, and treated this data set it in the same way as an experimentalist would treat a batch of potentially compromised experiments or expired reagents.

6The actual scoring is slightly more complicated because a small portion of annotations provided by experts belonged to the class uncertain (corresponding to the option for evaluators “Unable to decide”), which was viewed as an intermediate between classes correct and incorrect-such annotation received a score of 0.

7We used 68 features, most of which are non-binary, see Table 2.5.

8The only exception occurs when the features are truly conditionally independent of one another. In this special case both methods should have an identical performance. The same reasoning applies to all other approximations of the full Bayesian analysis (Clustered Bayes, Discriminant Analysis, and Maximum Entropy methods): They should perform less accurately than the full Bayesian analysis whenever their assumptions are not matched exactly by the data, and perform identically to the full Bayesian analysis otherwise.

9These assumptions lead to a linear optimal decision boundary, as reflected by the name of the method.

10This change in assumptions leads to a quadratic optimal decision boundary.

11For example, if the original space is two-dimensional x = (x1, x2) and the degree d of the polynomial kernel is 2, the implicit transformation is h(x) = (1, Ö2x1, Ö2x2, x12, x22, Ö2x1x2).

12That is, we obtained estimates of the prior probabilities of classes in Equation 2.3, P(C=ccorrect) = 0.65 and P(C=cincorrect) = 0.35. The raw precision that we report here is much lower than estimates that we reported in earlier studies. At least three factors contributed to this discrepancy. First, we performed previous evaluations for individual components of the system, rather than over the whole text-mining pipeline. Second, after we performed our previous evaluation more than 2 years ago, we expanded substantially the list of relation types/verbs handled by the system; the most recently added relations clearly contributed to the increased error rate. Third, we performed the earlier evaluations using data sets that were at least two orders of magnitude smaller than those reported in the present study. In addition, these smaller data sets were generated by sampling the most popular of the extracted facts-these more popular statements probably tend to be easier to extract correctly automatically.

13Alan M. Turing proposed an experiment for testing machine intelligence by interrogating the machine and a group of humans through a mediator, the goal being to distinguish the humans from the machine on the basis of only typewritten replies. If the interrogator fails to make the correct distinction, machine intelligence passes the test. Applying the Turing test to our problem, we imagine that we have a group of four evaluators, one of which is a computer. If we cannot single out the computer-embodied evaluator on the basis of a higher error rate, our artificial evaluator passes the test.

14Recall is defined as [(number of true positives)/(number of true positives+number of false negatives)]. The false-negative results-the facts that were in the original text but were missed by the system-can be generated at two stages of the analysis. The first stage, information extraction, occurs when the GeneWays pipeline recovers facts with recall b-we did not try to measure the value of b in the present study. The second stage is the automated curation of the database, during which all facts with score below a certain threshold are discarded. This second stage is associated with an additional loss of the recall: Only a proportion, r, of the originally extracted true-positive facts is retained. It is the second type of recall that we discuss in the text (see Figure 2.10). The overall recall, including both stages of the analysis, is just a product of the two values, br.

15We also analyzed the relation between the size of the training data set and the accuracy of MaxEnt 2 method. While accuracy of the MaxEnt 2 method with the whole training data set was 87.97%, it dropped to 87.38% when using only 60% of the training data, and to 83.57% with 20% of the training data.

16Discussing the philosophical meaning of “proper name” is outside of the scope of this text, but a simple rule of thumb might be the uniqueness implied by a proper name. For example, “horse” is a common name while “Bucephalus”, Alexander The Great’s horse, is a proper name. Protein names do not seem to conform, generally, to this uniqueness constrain. Moreover, notice that, in English, proper names are often capitalized (though you may consider exceptions like the poet name “e.e. cummings”).

17A pre-NER biomedical term recognition study like [125] is so different to posterior work that it shows how influential NER became.

18There is an on-going effort in defining how term boundaries should be set for certain biomedical term classes at the European Bioinformatics Institute, called “A framework for named entity annotations and interoperability of text mining components.” The effort is led by the Rebholz group.

19This analogous to pattern matching techniques described in Section 3.1.4.

20Token is used as a more general term than word but in many cases it is interchangeable.

21This is an easier task than identifying all terms, as explained in Chapter sec:introduction2.

22Chinese encyclopedia, “The Celestial Emporium of Benevolent Knowledge”. Translation by Franz Kuhn. The quote is in an essay by Jorge Luis Borges [200]. There is no other known reference to this encyclopedia and this has fueled speculation that is a fictional quote even if Borges’s essay is, otherwise, non-fiction. A famous reference to this quote is by Michel Foucault in “Les Mots et les choses: Une archéologie des sciences humaines.” [201].

23In its second entry, Merriam-Webster dictionary defines the word syntax as: “A connected or orderly system : harmonious arrangement of parts or elements `the syntax of classical architecture’ ”

24There are several KWIC tools, one of them being very similar to snippet classification.

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