Effective bio-event extraction using trigger words and syntactic dependencies

Halil Kilicoglu, Sabine Bergler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)583-609
Number of pages27
JournalComputational Intelligence
Volume27
Issue number4
DOIs
StatePublished - Nov 1 2011
Externally publishedYes

Keywords

  • biological event extraction
  • BioNLP
  • dependency parsing
  • heuristic system
  • negation detection
  • speculation recognition

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence

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