  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.
