2019/05/31 by Won-Ik Cho, Won Ik Cho, Seok Min Kim +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1905.13656
PACLIC 33 Camera-ready
openalex publication_date 2019/05/31 · arxiv created 2019/09/19 · arxiv updated 2019/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Different from the writing systems of many Romance and Germanic languages, some languages or language families show complex conjunct forms in character composition. For such cases where the conjuncts consist of the components representing consonant(s) and vowel, various character encoding schemes can be adopted beyond merely making up a one-hot vector. However, there has been little work done on intra-language comparison regarding performances using each representation. In this study, utilizing the Korean language which is character-rich and agglutinative, we investigate an encoding scheme that is the most effective among Jamo-level one-hot, character-level one-hot, character-level dense, and character-level multi-hot. Classification performance with each scheme is evaluated on two corpora: one on binary sentiment analysis of movie reviews, and the other on multi-class identification of intention types. The result displays that the character-level features show higher performance in general, although the Jamo-level features may show compatibility with the attention-based models if guaranteed adequate parameter set size.