@article{M917417DA,
title = "Syllable-based Korean POS Tagging Based on Combining a Pre-analyzed Dictionary with Machine Learning",
journal = "Journal of KIISE, JOK",
year = "2016",
issn = "2383-630X",
doi = "",
author = "Chung-Hee Lee,Joon-Ho Lim,Soojong Lim,Hyun-Ki Kim",
keywords = "morphological analysis,POS tagging,machine learning,pre-analyzed dictionary",
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."
}