ME-based biomedical named entity recognition using lexical knowledge

Kyung Mi Park, Seon Ho Kim, Hae Chang Rim, Young Sook Hwang

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


In this paper, we present a two-phase biomedical NE-recognition method based on a ME model: we first recognize biomedical terms and then assign appropriate semantic classes to the recognized terms. In the two-phase NE-recognition method, the performance of the term-recognition phase is very important, because the semantic classification is performed on the region identified at the recognition phase. In this study, in order to improve the performance of term recognition, we try to incorporate lexical knowledge into pre- and postprocessing of the term-recognition phase. In the preprocessing step, we use domain-salient words as lexical knowledge obtained by corpus comparison. In the postprocessing step, we utilize χ 2-based collocations gained from Medline corpus. In addition, we use morphological patterns extracted from the training data as features for learning the ME-based classifiers. Experimental results show that the performance of NE-recognition can be improved by utilizing such lexical knowledge.

Original languageEnglish
Pages (from-to)4-21
Number of pages18
JournalACM Transactions on Asian Language Information Processing
Issue number1
Publication statusPublished - 2006


  • Biomedical term recognition
  • Collocations
  • Maximum-entropy model
  • Morphological patterns
  • Postprocessing
  • Preprocessing
  • Salient words
  • Semantic classification

ASJC Scopus subject areas

  • Computer Science(all)


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