Journal of Electrical Engineering and information Science

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

A Hidden Markov Model Imbedding Multiword Units for Part-of-Speech Tagging

  • Kim, Jae-Hoon (Division of Automation and Information Engineering, Korea Maritime University) ;
  • Jungyun Seo (Department of Computer Science, Sogang University)
  • 발행 : 1997.12.01
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초록

Morphological Analysis of Korean has known to be a very complicated problem. Especially, the degree of part-of-speech(POS) ambiguity is much higher than English. Many researchers have tried to use a hidden Markov model(HMM) to solve the POS tagging problem and showed arround 95% correctness ratio. However, the lack of lexical information involves a hidden Markov model for POS tagging in lots of difficulties in improving the performance. To alleviate the burden, this paper proposes a method for combining multiword units, which are types of lexical information, into a hidden Markov model for POS tagging. This paper also proposes a method for extracting multiword units from POS tagged corpus. In this paper, a multiword unit is defined as a unit which consists of more than one word. We found that these multiword units are the major source of POS tagging errors. Our experiment shows that the error reduction rate of the proposed method is about 13%.

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