A heuristic method based on a statistical approach for Chinese text segmentation

  • Authors:

  • Christopher C. Yang;K. W. Li

  • Affiliations:

  • Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR;Department of Systems Engineering & Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2005

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Abstract

The authors propose a heuristic method for Chinese automatic text segmentation based on a statistical approach. This method is developed based on statistical information about the association among adjacent characters in Chinese text. Mutual information of bi-grams and significant estimation of tri-grams are utilized. A heuristic method with six rules is then proposed to determine the segmentation points in a Chinese sentence. No dictionary is required in this method. Chinese text segmentation is important in Chinese text indexing and thus greatly affects the performance of Chinese information retrieval. Due to the lack of delimiters of words in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, segmentation ambiguities and occurrences of out-of-vocabulary words (i.e., unknown words) are the major challenges in Chinese segmentation. Many research studies dealing with the problem of word segmentation have focused on the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. The experimental result shows that the proposed heuristic method is promising to segment the unknown words as well as the known words. The authors further investigated the distribution of the errors of commission and the errors of omission caused by the proposed heuristic method and benchmarked the proposed heuristic method with a previous proposed technique, boundary detection. It is found that the heuristic method outperformed the boundary detection method.