Using a Smoothing Maximum Entropy Model for Chinese Nominal Entity Tagging

  • Jinying Chen
  • Nianwen Xue
  • Martha Palmer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)


This paper treats nominal entity tagging as a six-way (five categories plus non-entity) classification problem and applies a smoothing maximum entropy (ME) model with a Gaussian prior to a Chinese nominal entity tagging task. The experimental results show that the model performs consistently better than an ME model using a simple count cut-off. The results also suggest that simple semantic features extracted from an electronic dictionary improve the model’s performance, especially when the training data is insufficient.


Maximum Entropy Semantic Category Semantic Feature Maximum Entropy Model Current Word 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jinying Chen
    • 1
  • Nianwen Xue
    • 1
  • Martha Palmer
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaUSA

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