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

Authors:
Syed Toufeeq Ahmed;Radhika Nair;Chintan Patel;Sheela P. Kanwar;Jörg Hakenberg;Hasan Davulcu
Affiliations:
Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ;Arizona State University, Tempe, AZ
Venue:
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Year:
2010

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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. An online version of BioEve is available at http://bioeve.org/.