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

15 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 publicationNatural Language Processing - IJCNLP 2005 - Second International Joint Conference, Proceedings
PublisherSpringer Verlag
Pages646-657
Number of pages12
ISBN (Print)3540291725, 9783540291725
DOIs
Publication statusPublished - 2005
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)0302-9743
ISSN (Electronic)1611-3349

Other

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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