2018/06/28 by Andrew Matteson, Andrew Stuart Matteson, Chanhee Lee +7
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1806.10771
10 pages, 6 figures, accepted as a conference paper at COLING 2018
arxiv created 2018/06/28 · openalex publication_date 2018/06/28 · arxiv updated 2018/06/29 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.