The Japanese Society for Artificial Intelligence

In this paper, we present a novel method for learning word embeddings. However, several word embedding approaches with extracting subwords from target word have been proposed, those methods have the problem of leaving subwords without meaning associated with the target word. These subwords have negative effects on obtaining better performance of word embeddings. To solve this problem, we adopted switching subword extraction rules based on Japanese character types. With this contrivance, the appearence of the subwords are surpressed. As a result, our method achieved better results on word similarity task than Word2Vec and FastText.