Semantic classification and dependency parsing enabled automated bio-molecular event extraction from text

Syed Toufeeq Ahmed, Radhika Nair, Chintan Patel, Sheela P. Kanwar, Jörg Hakenberg, Hasan Davulcu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The last two decades of invigorating research in the area of human genome sequencing marked the beginning of large-scale data collection. Much of the valuable knowledge gained is found in published articles, and thus in un-structured textual form. To aid in searching and extracting knowledge from textual sources, we present BioEve, a fully automated system to extract bio-molecular events from Medline abstracts. BioEve first semantically classifies each sentence to the class type of the event mentioned in the sentence, and then using high coverage, class-specific, hand-crafted rules, it extracts the participants of that event.

Original languageEnglish (US)
Title of host publication2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
Pages370-373
Number of pages4
DOIs
StatePublished - Oct 25 2010
Event2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 - Niagara Falls, NY, United States
Duration: Aug 2 2010Aug 4 2010

Publication series

Name2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010

Other

Other2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
CountryUnited States
CityNiagara Falls, NY
Period8/2/108/4/10

Keywords

  • Bio-molecular event extraction
  • Dependency parsing
  • Information extraction
  • Semantic classification

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Information Management

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