Abstract
Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i.e., a dictionary of known morphological transformation forms, or actions). These models have been created with the assumption that character-level, dictionary-less morphological analysis was intractable due to the number of actions required. We present, in this study, a multi-stage action-based model that can perform morphological transformation and part-of-speech tagging using arbitrary units of input and apply it to the case of character-level Korean morphological analysis. Among models that do not employ prior linguistic knowledge, we achieve state-of-the-art word and sentence-level tagging accuracy with the Sejong Korean corpus using our proposed data-driven Bi-LSTM model.- Anthology ID:
- C18-1210
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2482–2492
- Language:
- URL:
- https://aclanthology.org/C18-1210
- DOI:
- Cite (ACL):
- Andrew Matteson, Chanhee Lee, Youngbum Kim, and Heuiseok Lim. 2018. Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2482–2492, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging (Matteson et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/C18-1210.pdf