  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.
