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Distribution Of Multi-Words In Chinese And English Documents

Author

Listed:
  • WEN ZHANG

    (School of Knowledge Science, Japan Advanced Institute, of Science and Technology, 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan;
    Laboratory of Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • TAKETOSHI YOSHIDA

    (School of Knowledge Science, Japan Advanced Institute, of Science and Technology, 1-1 Asahidai, Tatsunokuchi, Ishikawa 923-1292, Japan)

  • XIJIN TANG

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China)

Abstract

As a hybrid of N-gram in natural language processing and collocation in statistical linguistics, multi-word is becoming a hot topic in area of text mining and information retrieval. In this paper, a study concerning distribution of multi-words is carried out to explore a theoretical basis for probabilistic term-weighting scheme. Specifically, the Poisson distribution, zero-inflated binomial distribution, and G-distribution are comparatively studied on a task of predicting probabilities of multi-words' occurrences using these distributions, for both technical multi-words and nontechnical multi-words. In addition, a rule-based multi-word extraction algorithm is proposed to extract multi-words from texts based on words' occurring patterns and syntactical structures. Our experimental results demonstrate that G-distribution has the best capability to predict probabilities of frequency of multi-words' occurrence and the Poisson distribution is comparable to zero-inflated binomial distribution in estimation of multi-word distribution. The outcome of this study validates that burstiness is a universal phenomenon in linguistic count data, which is applicable not only for individual content words but also for multi-words.

Suggested Citation

  • Wen Zhang & Taketoshi Yoshida & Xijin Tang, 2009. "Distribution Of Multi-Words In Chinese And English Documents," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 249-265.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:02:n:s0219622009003399
    DOI: 10.1142/S0219622009003399
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    Citations

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    Cited by:

    1. Gang Kou & Chunwei Lou, 2012. "Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data," Annals of Operations Research, Springer, vol. 197(1), pages 123-134, August.
    2. Tomaz Bartol & Karmen Stopar, 2015. "Nano language and distribution of article title terms according to power laws," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 435-451, May.

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