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EFFECTIVE BIO-EVENT EXTRACTION USING TRIGGER WORDS AND SYNTACTIC DEPENDENCIES

2011; Wiley; Volume: 27; Issue: 4 Linguagem: Inglês

10.1111/j.1467-8640.2011.00401.x

1467-8640

Halil Kilicoglu, Sabine Bergler,

Natural Language Processing Techniques

Computational IntelligenceVolume 27, Issue 4 p. 583-609 EFFECTIVE BIO-EVENT EXTRACTION USING TRIGGER WORDS AND SYNTACTIC DEPENDENCIES Halil Kilicoglu, Halil Kilicoglu Department of Computer Science and Software Engineering, Concordia University, Montréal, CanadaSearch for more papers by this authorSabine Bergler, Sabine Bergler Department of Computer Science and Software Engineering, Concordia University, Montréal, CanadaSearch for more papers by this author Halil Kilicoglu, Halil Kilicoglu Department of Computer Science and Software Engineering, Concordia University, Montréal, CanadaSearch for more papers by this authorSabine Bergler, Sabine Bergler Department of Computer Science and Software Engineering, Concordia University, Montréal, CanadaSearch for more papers by this author First published: 27 November 2011 https://doi.org/10.1111/j.1467-8640.2011.00401.xCitations: 13 Halil Kilicoglu, Concordia University, Department of Computer Science and Software Engineering, 1455 de Maisonneuve Blvd West, Montréal, QC H3G 1M8, Canada; e-mail: [email protected] Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract The scientific literature is the main source for comprehensive, up-to-date biological knowledge. Automatic extraction of this knowledge facilitates core biological tasks, such as database curation and knowledge discovery. We present here a linguistically inspired, rule-based and syntax-driven methodology for biological event extraction. We rely on a dictionary of trigger words to detect and characterize event expressions and syntactic dependency based heuristics to extract their event arguments. We refine and extend our prior work to recognize speculated and negated events. We show that heuristics based on syntactic dependencies, used to identify event arguments, extend naturally to also identify speculation and negation scope. In the BioNLP'09 Shared Task on Event Extraction, our system placed third in the Core Event Extraction Task (F-score of 0.4462), and first in the Speculation and Negation Task (F-score of 0.4252). Of particular interest is the extraction of complex regulatory events, where it scored second place. Our system significantly outperformed other participating systems in detecting speculation and negation. These results demonstrate the utility of a syntax-driven approach. In this article, we also report on our more recent work on supervised learning of event trigger expressions and discuss event annotation issues, based on our corpus analysis. REFERENCES Ahlers, C. B., M. Fiszman, D. Demner-Fushman, F. M. Lang, and T. C. Rindflesch. 2007. Extracting semantic predications from Medline citations for pharmacogenomics. In Pacific Symposium on Biocomputing, Maui, HI, pp. 209–220. Airola, A., S. Pyysalo, J. Björne, T. Pahikkala, F. Ginter, and T. Salakoski. 2008. 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Briefings in Bioinformatics, 8(5): 358–375. Citing Literature Volume27, Issue4November 2011Pages 583-609 ReferencesRelatedInformation