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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, S., Yoon, J., Park, KM., Rim, HC. (2005). Two-Phase Biomedical Named Entity Recognition Using A Hybrid Method. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_57
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DOI: https://doi.org/10.1007/11562214_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
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