A robust transformation-based learning approach using ripple down rules for part-of-speech tagging

Dat Quoc Nguyen*, Dai Quoc Nguyen, Dang Duc Pham, Son Bao Pham

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.

Original languageEnglish
Pages (from-to)409-422
Number of pages14
JournalAI Communications
Volume29
Issue number3
DOIs
Publication statusPublished - 26 Apr 2016

Keywords

  • Bulgarian
  • Czech
  • Dutch
  • English
  • French
  • German
  • Hindi
  • Italian
  • Morphological tagging
  • Natural language processing
  • Part-of-speech tagging
  • Portuguese
  • RDRPOSTagger
  • Rule-based POS tagger
  • Single classification ripple down rules
  • Spanish
  • Swedish
  • Thai
  • Vietnamese

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