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

Morphological Analysis of Japanese Hiragana Sentences using the BI-LSTM CRF Model