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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tsai, T.H., Chou, W.C., Wu, S.H., Sung, T.Y., Hsiang, J., Hsu, W.L.: Integrating Linguistic Knowledge into a Conditional Random Field Framework to Identify Biomedical Named Entities. Expert Systems with Applications 30(1), 117–128 (2006)CrossRefGoogle Scholar
  2. 2.
    Hirschman, L., Yeh, A., Blaschke, C., Valencia, A.: Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics 6(Suppl 1) (2005)Google Scholar
  3. 3.
    Kim, J.D., Ohta, T., Tsuruoka, Y., Tateisi, Y.: Introduction to the Bio-Entity Recognition Task at JNLPBA. In: Joint Workshop on Natural Language Processing in Biomedicine and its Applications, pp. 70–75 (2004)Google Scholar
  4. 4.
    Kou, Z., Cohen, W.W., Murphy, R.F.: High-recall protein entity recognition using a dictionary. Bioinformatics 21(Suppl. 1), i266–i273 (2005)CrossRefGoogle Scholar
  5. 5.
    Cohen, A.M., Hersh, W.R.: A survey of current work in biomedical text mining. Bridfings In Bioinformatics 6(1), 57–71 (2005)CrossRefGoogle Scholar
  6. 6.
    Zhou, G.D., Su, J.: Exploring Deep Knowledge Resources in Biomedical Name Recognition. In: Joint Workshop on Natural Language Processing in Biomedicine and its Applications, pp. 96–99 (2004)Google Scholar
  7. 7.
    Kazama, J., Makino, T., Ohta, Y., Tsujii, J.: Tuning Support Vector Machines for Biomedical Named Entity Recognition. In: Proceedings of the ACL Workshop on Natural Language Processing in the Biomedical Domain, pp. 1–8 (2002)Google Scholar
  8. 8.
    Finkel, J., Dingare, S., Nguyen, H., Nissim, M., Manning, C., Sinclair, G.: Exploiting Context for Biomedical Entity Recognition: From Syntax to the Web. In: Joint Workshop on Natural Language Processing in Biomedicine and its Applications, pp. 88–91 (2004)Google Scholar
  9. 9.
    Burr, S.: Biomedical Named Entity Recognition Using Conditional Random Fields and Novel Feature Sets. In: Joint Workshop on Natural Language Processing in Biomedicine and its Application, pp. 104–107 (2004)Google Scholar
  10. 10.
    Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of HLT-NAACL, pp. 213–220 (2003)Google Scholar
  11. 11.
    Shatkay, H., Feldman, R.: Mining the Biomedical Literature in the Genomic Era: An Overview. Journal Of Computational Biology 10(6), 821–855 (2003)CrossRefGoogle Scholar
  12. 12.
    Yeh, A.S., Morgan, A., Colosimo, M., Hirschman, L.: BioCreAtIvE task 1A: gene mention finding evaluation. BMC Bioinformatics 6(Suppl 1) (2005)Google Scholar
  13. 13.
    Tsai, T.H., Wu, C.W., Hsu, W.L.: Using Maximum Entropy to Extract Biomedical Named Entities without Dictionaries. In: Proceedings of IJCNL, pp. 270–275 (2005)Google Scholar
  14. 14.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proceedings of the International Conference on Machine Learning, pp. 282–289 (2001)Google Scholar
  15. 15.
    Sutton, C., McCallum, A.: An Introduction to Conditional Random Fields for Relational Learning (2005),
  16. 16.
    Wallach, H.M.: Efficient training of conditional random fields. Master’s thesis. University of Edinburgh (2002)Google Scholar
  17. 17.
    McCallum, A.: MALLET: A Machine Learning for Language Toolkit (2002),
  18. 18.
    Tsuruoka, Y., Tateishi, Y., Kim, J.D.: Developing a Robust Part-of-Speech Tagger for Biomedical Text. In: Advances in Informatics - 10th Panhellenic Conference on Informatics, pp. 382–392 (2005)Google Scholar
  19. 19.
    Erik, F., Sang, T.K., Buchholz, S.: Introduction to the CoNLL-2000 Shared Task: Chunking. In: Proceedings of CoNLL-2000 and LLL-2000, pp. 127–132 (2000)Google Scholar

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

Personalised recommendations