TY  - JOUR
T1  - Research on Joint Models for Korean Word Spacing and POS (Part-Of-Speech) Tagging based on Bidirectional LSTM-CRF
AU  - Kim, Seon-Wu 
AU  - Choi, Sung-Pil 
JO  - Journal of KIISE, JOK
PY  - 2018
DA  - 2018/1/14
DO  - 10.5626/JOK.2018.45.8.792
KW  - korean POS(part-of-speech) tagging
KW  - korean automatic spacing
KW  - NLP(natural language processing)
KW  - deep-learning
AB  - In general, Korean part-of-speech tagging is done on a sentence in which the spacing is completed by a word as an input. In order to process a sentence that is not properly spaced, automatic spacing is needed to correct the error. However, if the automatic spacing and the parts tagging are sequentially performed, a serious performance degradation may result from an error occurring at each step. In this study, we try to solve this problem by constructing an integrated model that can perform automatic spacing and POS(Part-Of-Speech) tagging simultaneously. Based on the Bidirectional LSTM-CRF model, we propose an integrated model that can simultaneously perform syllable-based word spacing and POS tagging complementarily. In the experiments using a Sejong tagged text, we obtained 98.77% POS tagging accuracy for the completely spaced sentences, and 97.92% morpheme accuracy for the sentences without any word spacing.