TY  - JOUR
T1  - Sequence-to-sequence based Morphological Analysis and Part-Of-Speech Tagging for Korean Language with Convolutional Features
AU  - Li, Jianri 
AU  - Lee, EuiHyeon 
AU  - Lee, Jong-Hyeok 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 
KW  - morphological analysis
KW  - POS Tagging
KW  - sequence-to-sequence model
AB  - Traditional Korean morphological analysis and POS tagging methods usually consist of two steps: 1 Generat hypotheses of all possible combinations of morphemes for given input, 2 Perform POS tagging search optimal result. require additional resource dictionaries and step could error to the step. In this paper, we tried to solve this problem end-to-end fashion using sequence-to-sequence model convolutional features. Experiment results Sejong corpus sour approach achieved 97.15% F1-score on morpheme level, 95.33% and 60.62% precision on word and sentence level, respectively; s96.91% F1-score on morpheme level, 95.40% and 60.62% precision on word and sentence level, respectively.