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Conditional Random Fields Based Label Sequence and Information Feedback

  • Wei Jiang
  • Yi Guan
  • Xiao-Long Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Part-of-speech (POS) tagging and shallow parsing are sequence modeling problems. While HMM and other generative models are not the most appropriate for the task of labeling sequential data. Compared with HMM, Maximum Entropy Markov models (MEMM) and other discriminative finite-state models can easily fused more features, however they suffer from the label bias problem. This paper presents a method of Chinese POS tagging and shallow parsing based on conditional random fields (CRF), as new discriminative sequential models, which may incorporate many rich features and well avoid the label bias problem. Moreover, we propose the information feedback from syntactical analysis to lexical analysis, since natural language should be a multi-knowledge interaction in nature. Experiments show that CRF approach achieves 0.70% F-score improvement in POS tagging and 0.67% improvement in shallow parsing. And we also confirm the effectiveness of information feedback to some complicated multi-class words.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Jiang
    • 1
  • Yi Guan
    • 1
  • Xiao-Long Wang
    • 1
  1. 1.School of Computer Science and Technology, Harbin Institute of Technology, 150001, HarbinChina

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