A probabilistic feature based maximum entropy model for chinese named entity recognition

  • Authors:

  • Suxiang Zhang;Xiaojie Wang;Juan Wen;Ying Qin;Yixin Zhong

  • Affiliations:

  • School of Information Engineering of Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering of Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering of Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering of Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering of Beijing University of Posts and Telecommunications, Beijing, China

  • Venue:
  • ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
  • Year:
  • 2006

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Abstract

This paper proposes a probabilistic feature based Maximum Entropy (ME) model for Chinese named entity recognition. Where, probabilistic feature functions are used instead of binary feature functions, it is one of the several differences between this model and the most of the previous ME based model. We also explore several new features in our model, which includes confidence functions, position of features etc. Like those in some previous works, we use sub-models to model Chinese Person Names, Foreign Names, location name and organization name respectively, but we bring some new techniques in these sub-models. Experimental results show our ME model combining above new elements brings significant improvements.