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Apply a Rough Set-Based Classifier to Dependency Parsing

  • Yangsheng Ji
  • Lin Shang
  • Xinyu Dai
  • Ruoce Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5009)

Abstract

A rough set-based semi-naive Bayesian classification method is applied to dependency parsing, which is an important task in syntactic structure analysis of natural language processing. Many parsing algorithms have emerged combined with statistical machine learning techniques. The rough set-based classifier is embedded with Nivre’s deterministic parsing algorithm to conduct dependency parsing task on a Chinese corpus. Experimental results show that the method has a good performance on dependency parsing task. Moreover, the experiments have justified the effectiveness of the classification influence.

Keywords

Rough set Attribute dependency Semi-naive Bayesian classifier Dependency parsing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yangsheng Ji
    • 1
  • Lin Shang
    • 1
  • Xinyu Dai
    • 1
  • Ruoce Ma
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityChina

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