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How to utilize syllable distribution patterns as the input of LSTM for Korean morphological analysis.
- Source :
-
Pattern Recognition Letters . Apr2019, Vol. 120, p39-45. 7p. - Publication Year :
- 2019
-
Abstract
- Abstract This paper proposes the use of syllable distribution patterns as deep learning inputs for morphological analysis. The proposed syllable distribution pattern comprises two parts: a distributed syllable embedding vector and a morpheme syllable-level distribution pattern. As a learning method, we utilize bidirectional long short-term memory with a conditional random field layer (Bi-LSTM-CRF) for Korean part-of-speech tagging tasks. After syllable-level outputs are generated by Bi-LSTM-CRF, a morpheme restoration process is performed utilizing pre-analyzed dictionaries that were automatically created from a training corpus. Experimental results reveal outstanding performance for the proposed method with an F1-score of 98.65%. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PATTERN perception
*DEEP learning
*SHORT-term memory
*MORPHEMICS
*TASK performance
Subjects
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 120
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 134883428
- Full Text :
- https://doi.org/10.1016/j.patrec.2018.12.019