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Tagging Complex NEs with MaxEnt Models: Layered Structures Versus Extended Tagset

  • Deyi Xiong
  • Hongkui Yu
  • Qun Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

The paper discusses two policies for recognizing NEs with complex structures by maximum entropy models. One policy is to develop cascaded MaxEnt models at different levels. The other is to design more detailed tags with human knowledge in order to represent complex structures. The experiments on Chinese organization names recognition indicate that layered structures result in more accurate models while extended tags can not lead to positive results as expected. We empirically prove that the {start, continue, end, unique, other} tag set is the best tag set for NE recognition with MaxEnt models.

Keywords

Cascade Model MaxEnt Model Multilevel Structure Maximum Entropy Model Context Window 
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

  • Deyi Xiong
    • 1
    • 2
  • Hongkui Yu
    • 1
    • 4
  • Qun Liu
    • 1
    • 3
  1. 1.Institute of Computing Technologythe Chinese Academy of SciencesBeijing
  2. 2.Graduate School of the Chinese Academy of Sciences 
  3. 3.Inst. of Computational LinguisticsPeking UniversityBeijing
  4. 4.Information science & technology collegeBeijing University of Chemical TechnologyBeijing

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