Korean Part-of-Speech Tagging Based on a Hidden Markov Model (1997)
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BibTeX
@MISC{Kim97koreanpart-of-speech,
author = {Jae-hoon Kim and Chul-su Lim and Jungyun Seo},
title = {Korean Part-of-Speech Tagging Based on a Hidden Markov Model},
year = {1997}
}
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Abstract
In this paper, we describe a method for assigning a part-of-speech tag in Korean to each morpheme. The method is based on a hidden Markov model which can be trained without using any tagged corpus. To relax the amount of computation to process multiple observation sequences, which are extraordinarily occurred in Korean part-of-speech tagging, we develop a revised Viterbi algorithm for determining the most promising tag sequence. Experimental results show that the accuracy of the model is approximately 90% on average. The performance is no better than those of English tagging systems. This is due to the partially free word order feature of Korean and the lack of training data. However, to the best of our knowledge, this is the first Korean POS tagging system which can be trained without using tagged corpus. 1: Introduction Many words have ambiguous part-of-speech(POS) tags. For example, `time' in English can be a noun, a verb, or an adjective. In many cases, such ambiguities can be re...
