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Learning Parameters of Japanease Morphological Analyzer based-on Hidden Markov Model

  • TAKEUCHI Kouichi
    Graduate School of Information Science, Nara Institute of Science and Technology
  • MATSUMOTO Yuji
    Graduate School of Information Science, Nara Institute of Science and Technology

Bibliographic Information

Other Title
  • HMMによる日本語形態素解析システムのパラメータ学習

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Abstract

Morphological analysis is the first step toward the analysis of Japanese texts and one of the most important processes. So far, we have been developing the Japanease morphological analyzer JUMAN as a public-domain system. In JUMAN, ambiguities of morphological analysis are reduced by means of costs manually attached to the connectivity rules and words. The performance of JUMAN largely depends on those manually attached costs, while at present JUMAN has no facility to optimize the costs. This paper proposes a method for optimizing the costs (i.e. parameters) to be attached to the connectivity rules and words. The proposed method is based on hidden Markov model, which has proved effective in parameter estimation of English part-of-speech tagging. The result of experiments shows that the proposed optimization method improves the manually attached parameters.

Journal

  • IPSJ SIG Notes

    IPSJ SIG Notes 108 13-19, 1995-07-20

    Information Processing Society of Japan (IPSJ)

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Details

  • CRID
    1571980077130651392
  • NII Article ID
    110002935039
  • NII Book ID
    AN10115061
  • Text Lang
    ja
  • Data Source
    • CiNii Articles

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