A maximum entropy-based bio-molecular event extraction model that considers event generation

Hyoung Gyu Lee, So Young Park, Hae-Chang Rim, Do Gil Lee, Hong Woo Chun

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we propose a maximum entropy-based model, which can mathematically explain the biomolecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.

Original languageEnglish
Pages (from-to)248-265
Number of pages18
JournalJournal of Information Processing Systems
Volume11
Issue number2
DOIs
Publication statusPublished - 2014

Keywords

  • Bioinformatics
  • Event extraction
  • Maximum entropy
  • Text-mining

ASJC Scopus subject areas

  • Information Systems
  • Software

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