Semantic classification of bio-entities incorporating predicate-argument features

Kyung Mi Park, Hae-Chang Rim

Research output: Contribution to journalArticle

1 Citation (Scopus)


In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicateargument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.

Original languageEnglish
Pages (from-to)1211-1214
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number4
Publication statusPublished - 2008 Apr 1



  • Biomedical verb
  • Maximum entropy model
  • Predicate-argument feature
  • Semantic classification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Software
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition

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