Biomedical Named Entities Recognition Using Conditional Random Fields Model

  • Chengjie Sun
  • Yi Guan
  • Xiaolong Wang
  • Lei Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


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.


Natural Language Processing Conditional Random Field Biomedical Literature Name Entity Recognition Entity Recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chengjie Sun
    • 1
  • Yi Guan
    • 1
  • Xiaolong Wang
    • 1
  • Lei Lin
    • 1
  1. 1.School of Computer ScienceHarbin Institute of TechnologyChina

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