Abstract
Biomedical named entity recognition is a critical task for automatically mining knowledge from biomedical literature. In this paper, we introduce Conditional Random Fields model to recognize biomedical named entities from biomedical literature. Rich features including literal, context and semantics are involved in Conditional Random Fields model. Shallow syntactic features are first introduced to Conditional Random Fields model and do boundary detection and semantic labeling at the same time, which effectively improve the model’s performance. Experiments show that our method can achieve an F-measure of 71.2% in JNLPBA test data and which is better than most of state-of-the-art system.
This work is supported by National Natural Science Foundation of China (60504021) and The 863 high Technology Research and Development Programme of China (2002AA117010-09).
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Sun, C., Guan, Y., Wang, X., Lin, L. (2006). Biomedical Named Entities Recognition Using Conditional Random Fields Model. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_159
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DOI: https://doi.org/10.1007/11881599_159
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