@article{MF041D0BB,
title = "Sequence-to-sequence based Morphological Analysis and Part-Of-Speech Tagging for Korean Language with Convolutional Features",
journal = "Journal of KIISE, JOK",
year = "2017",
issn = "2383-630X",
doi = "",
author = "Jianri Li,EuiHyeon Lee,Jong-Hyeok Lee",
keywords = "morphological analysis,POS Tagging,sequence-to-sequence model",
abstract = "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."
}