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Syllable-based Korean POS Tagging Based on Combining a Pre-analyzed Dictionary with Machine Learning
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 3,  2016, pp.362-369
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.3.362
 Title & Authors
Syllable-based Korean POS Tagging Based on Combining a Pre-analyzed Dictionary with Machine Learning
Lee, Chung-Hee; Lim, Joon-Ho; Lim, Soojong; Kim, Hyun-Ki;
 
 Abstract
This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.
 Keywords
morphological analysis;POS tagging;machine learning;pre-analyzed dictionary;
 Language
Korean
 Cited by
 References
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