Morphological Analysis of Japanese Hiragana Sentences using the BI-LSTM CRF Model
This addresses a domain-specific challenge in Japanese NLP for processing texts for children or those who cannot read Chinese characters, but it is incremental as it builds on existing methods.
This study tackled the problem of morphological analysis for Japanese Hiragana sentences, which is more difficult due to fewer word delimiters, by fine-tuning a Bi-LSTM CRF model based on ordinary Japanese text and examining the impact of training data from various genres.
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.