Two-phase biomedical named entity recognition using a hybrid method

Authors:
Seonho Kim;Juntae Yoon;Kyung-Mi Park;Hae-Chang Rim
Affiliations:
Dept. of Computer Science and Engineering, Korea University, Seoul, Korea;NLP Lab., Daumsoft Inc., Seoul, Korea;Dept. of Computer Science and Engineering, Korea University, Seoul, Korea;Dept. of Computer Science and Engineering, Korea University, Seoul, Korea
Venue:
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Year:
2005

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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.