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http://dx.doi.org/10.5391/JKIIS.2008.18.4.501

Part-Of-Speech Tagging using multiple sources of statistical data  

Cho, Seh-Yeong (명지대학교 컴퓨터소프트웨어학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.4, 2008 , pp. 501-506 More about this Journal
Abstract
Statistical POS tagging is prone to error, because of the inherent limitations of statistical data, especially single source of data. Therefore it is widely agreed that the possibility of further enhancement lies in exploiting various knowledge sources. However these data sources are bound to be inconsistent to each other. This paper shows the possibility of using maximum entropy model to Korean language POS tagging. We use as the knowledge sources n-gram data and trigger pair data. We show how perplexity measure varies when two knowledge sources are combined using maximum entropy method. The experiment used a trigram model which produced 94.9% accuracy using Hidden Markov Model, and showed increase to 95.6% when combined with trigger pair data using Maximum Entropy method. This clearly shows possibility of further enhancement when various knowledge sources are developed and combined using ME method.
Keywords
Maximum Entropy; Part of speech; N-gram; trigger pair;
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