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Word Extraction Based on Semantic Constraints in Chinese Word-Formation

  • Maosong Sun
  • Shengfen Luo
  • Benjamin K T’sou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3406)

Abstract

This paper presents a novel approach to Chinese word extraction based on semantic information of characters. A thesaurus of Chinese characters is conducted. A Chinese lexicon with 63,738 two-character words, together with the thesaurus of characters, are explored to learn semantic constraints between characters in Chinese word-formation, forming a semantic-tag-based HMM. The Baum-Welch re-estimation scheme is then chosen to train parameters of the HMM in the way of unsupervised learning. Various statistical measures for estimating the likelihood of a character string being a word are further tested. Large-scale experiments show that the results are promising: the F-score of this word extraction method can reach 68.5% whereas its counterpart, the character-based mutual information method, can only reach 47.5%.

Keywords

Hide Markov Model Chinese Character Associative Strength Chinese Word Unknown Word 
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

  • Maosong Sun
    • 1
  • Shengfen Luo
    • 1
  • Benjamin K T’sou
    • 2
  1. 1.National Lab. of Intelligent Tech. & SystemsTsinghua UniversityBeijingChina
  2. 2.Language Information Sciences Research CentreCity University of Hong Kong 

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