Ripple Down Rules for Part-of-Speech Tagging

  • Dat Quoc Nguyen
  • Dai Quoc Nguyen
  • Son Bao Pham
  • Dang Duc Pham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6608)

Abstract

This paper presents a new approach to learn a rule based system for the task of part of speech tagging. Our approach is based on an incremental knowledge acquisition methodology where rules are stored in an exception-structure and new rules are only added to correct errors of existing rules; thus allowing systematic control of interaction between rules. Experimental results of our approach on English show that we achieve in the best accuracy published to date: 97.095% on the Penn Treebank corpus. We also obtain the best performance for Vietnamese VietTreeBank corpus.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dat Quoc Nguyen
    • 1
  • Dai Quoc Nguyen
    • 1
  • Son Bao Pham
    • 1
    • 2
  • Dang Duc Pham
    • 1
  1. 1.Human Machine Interaction Laboratory, Faculty of Information TechnologyUniversity of Engineering and Technology, Vietnam National UniversityHanoiViet Nam
  2. 2.Information Technology InstituteVietnam National UniversityHanoiViet Nam

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