PubMed Health⌕ Search

Biomedical subjects

K Bretonnel Cohen

Publications and source records attributed to K Bretonnel Cohen.

7 recordsLinked to original sources
PubMed2006

A critical review of PASBio's argument structures for biomedical verbs.

BACKGROUND: Propositional representations of biomedical knowledge are a critical component of most aspects of semantic mining in biomedicine. However, the proper set of propositions has yet to be determined. Recently, the PASBio project proposed a set of propositions and argument structures for biomedical verbs. This initial set of representations presents an opportunity for evaluating the suitability of predicate-argument structures as a scheme for representing verbal semantics in the biomedical domain. Here, we quantitatively evaluate several dimensions of the initial PASBio propositional structure repository. RESULTS: We propose a number of metrics and heuristics related to arity, role labelling, argument realization, and corpus coverage for evaluating large-scale predicate-argument structure proposals. We evaluate the metrics and heuristics by applying them to PASBio 1.0. CONCLUSION: PASBio demonstrates the suitability of predicate-argument structures for representing aspects of the semantics of biomedical verbs. Metrics related to theta-criterion violations and to the distribution of arguments are able to detect flaws in semantic representations, given a set of predicate-argument structures and a relatively small corpus annotated with them.

Abstracting and Indexing↗
PubMed2006

Evaluation of lexical methods for detecting relationships between concepts from multiple ontologies.

We used exact term matching, stemming, and inclusion of synonyms, implemented via the Lucene information retrieval library, to discover relationships between the Gene Ontology and three other OBO ontologies: ChEBI, Cell Type, and BRENDA Tissue. Proposed relationships were evaluated by domain experts. We discovered 91,385 relationships between the ontologies. Various methods had a wide range of correctness. Based on these results, we recommend careful evaluation of all matching strategies before use, including exact string matching. The full set of relationships is available at compbio.uchsc.edu/dependencies.

Computational Biology↗
PubMed2006

Finding GeneRIFs via gene ontology annotations.

A Gene Reference Into Function (GeneRIF) is a concise phrase describing a function of a gene in the Entrez Gene database. Applying techniques from the area of natural language processing known as automatic summarization, it is possible to link the Entrez Gene database, the Gene Ontology, and the biomedical literature. A system was implemented that automatically suggests a sentence from a PubMed/MEDLINE abstract as a candidate GeneRIF by exploiting a gene's GO annotations along with location features and cue words. Results suggest that the method can significantly increase the number of GeneRIF annotations in Entrez Gene, and that it produces qualitatively more useful GeneRIFs than other methods.

Algorithms↗
PubMed2005

BioCreAtIvE task1A: entity identification with a stochastic tagger.

BACKGROUND: Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Where their system uses the Brill tagger, we used TnT, the Trigrams 'n' Tags HMM-based part-of-speech tagger. Based on careful error analysis, we implemented a set of post-processing rules to correct both false positives and false negatives. We participated in both the open and the closed divisions; for the open division, we made use of data from NCBI. RESULTS: Our base system without post-processing achieved a precision and recall of 68.0% and 77.2%, respectively, giving an F-measure of 72.3%. The full system with post-processing achieved a precision and recall of 80.3% and 80.5% giving an F-measure of 80.4%. We achieved a slight improvement (F-measure = 80.9%) by employing a dictionary-based post-processing step for the open division. We placed third in both the open and the closed division. CONCLUSION: Our results show that a part-of-speech tagger can be augmented with post-processing rules resulting in an entity identification system that competes well with other approaches.

Databases, Genetic↗
PubMed2005

Implications of compositionality in the gene ontology for its curation and usage.

In this paper we argue that a richer underlying representational model for the Gene Ontology that captures the implicit compositional structure of GO terms could have a positive impact on two activities crucial to the success of GO: ontology curation and database annotation. We show that many of the new terms added to GO in a one-year span appear to be compositional variations of other terms. We found that 90.2% of the 3,652 new terms added between July 2003 and July 2004 exhibited characteristics of compositionality. We also examine annotations available from the GO Consortium website that are either manually curated or automatically generated. We found that 74.5% and 63.2% of GO terms are seldom, if ever, used in manual and automatic annotations, respectively. We show that there are features that tend to distinguish terms that are used from those that are not. In order to characterize the effect of compositionality on the combinatorial properties of GO, we employ finite state automata that represent sets of GO terms. This representational tool demonstrates how ontologies can grow very fast, and also shows that small conceptual changes can directly result in a large number of changes to the terminology. We argue that the curation and annotation findings we report are influenced by the combinatorial properties that present themselves in an ontology that does not have a model that properly captures the compositional structure of its terms.

Computational Biology↗
PubMed2005

Empirical data on corpus design and usage in biomedical natural language processing.

This paper describes the design of six publicly available biomedical corpora. We then present usage data for the six corpora. We show that corpora that are carefully annotated with respect to structural and linguistic characteristics and that are distributed in standard formats are more widely used than corpora that are not. These findings have implications for the design of the next generation of biomedical corpora.

Computational Biology↗