  This study presents a Long Short-Term Memory (LSTM) neural network approach
to Japanese word segmentation (JWS). Previous studies on Chinese word
segmentation (CWS) succeeded in using recurrent neural networks such as LSTM
and gated recurrent units (GRU). However, in contrast to Chinese, Japanese
includes several character types, such as hiragana, katakana, and kanji, that
produce orthographic variations and increase the difficulty of word
segmentation. Additionally, it is important for JWS tasks to consider a global
context, and yet traditional JWS approaches rely on local features. In order to
address this problem, this study proposes employing an LSTM-based approach to
JWS. The experimental results indicate that the proposed model achieves
state-of-the-art accuracy with respect to various Japanese corpora.
