Two-phase biomedical named entity recognition using a hybrid method

Seonho Kim, Juntae Yoon, Kyung Mi Park, Hae-Chang Rim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical variations. This paper presents a two-phase biomedical NER consisting of term boundary detection and semantic labeling. By dividing the problem, we can adopt an effective model for each process. In our study, we use two exponential models, conditional random fields and maximum entropy, at each phase. Moreover, results by this machine learning based model are refined by rule-based postprocessing implemented using a finite state method. Experiments show it achieves the performance of F-score 71.19% on the JNLPBA 2004 shared task of identifying 5 classes of biomedical NEs.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages646-657
Number of pages12
Volume3651 LNAI
Publication statusPublished - 2005 Dec 1
Event2nd International Joint Conference on Natural Language Processing, IJCNLP 2005 - Jeju Island, Korea, Republic of
Duration: 2005 Oct 112005 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3651 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Joint Conference on Natural Language Processing, IJCNLP 2005
CountryKorea, Republic of
CityJeju Island
Period05/10/1105/10/13

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ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Kim, S., Yoon, J., Park, K. M., & Rim, H-C. (2005). Two-phase biomedical named entity recognition using a hybrid method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3651 LNAI, pp. 646-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3651 LNAI).