This study proposes a method to develop neural models of the morphological
analyzer for Japanese Hiragana sentences using the Bi-LSTM CRF model.
Morphological analysis is a technique that divides text data into words and
assigns information such as parts of speech. This technique plays an essential
role in downstream applications in Japanese natural language processing systems
because the Japanese language does not have word delimiters between words.
Hiragana is a type of Japanese phonogramic characters, which is used for texts
for children or people who cannot read Chinese characters. Morphological
analysis of Hiragana sentences is more difficult than that of ordinary Japanese
sentences because there is less information for dividing. For morphological
analysis of Hiragana sentences, we demonstrated the effectiveness of
fine-tuning using a model based on ordinary Japanese text and examined the
influence of training data on texts of various genres.

该研究提出了一种使用 Bi-LSTM CRF 模型开发日语 Hiragana 句子形态分析神经模型的方法，并证明了 fine-tuning 技术的有效性，并探究了训练数据对于文本不同类型的影响。