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 journalArticlepeer-review

    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 - 2015

    Bibliographical note

    Publisher Copyright:
    © 2015 KIPS.

    Keywords

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

    ASJC Scopus subject areas

    • Software
    • Information Systems

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