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)

Abstract

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.

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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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