Two-phase biomedical named entity recognition using a hybrid method

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Two-phase biomedical named entity recognition using a hybrid method (2005)

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by Seonho Kim , Kyung-mi Park

Venue:	In Proceedings of The Second International Joint Conference on Natural Language Processing (IJCNLP-05
Citations:	3 - 0 self

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@INPROCEEDINGS{Kim05two-phasebiomedical,
    author = {Seonho Kim and Kyung-mi Park},
    title = {Two-phase biomedical named entity recognition using a hybrid method},
    booktitle = {In Proceedings of The Second International Joint Conference on Natural Language Processing (IJCNLP-05},
    year = {2005},
    pages = {646--657}
}

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Abstract

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. 1

Keyphrases

entity recognition two-phase biomedical hybrid method finite state method rule-based postprocessing biomedical ne two-phase biomedical ner consisting conditional random field extensive lexical variation widespread ambiguity f-score 71 term boundary detection effective model exponential model biomedical information processing maximum entropy difficult problem semantic labeling

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