Generation of word embeddings for Japanese word sense disambiguate using paragraph embeddings in front and behind the target

Abstract

In recent years, using "word embeddings," in which vectors represent word meanings, has made it easier for computers to handle language meanings. However, Word Sense Disambiguation remains an issue for polysemous words. Word Sense Disambiguation determines which sense a polysemous word is used in a sentence. It is an essential task for computers to handle the meaning of language. For Japanese Word Sense Disambiguation, we propose a method to generate word embeddings of words so that the variance between clusters of different word senses is larger and the variance within each cluster is smaller. Our proposed model uses data before and after the target paragraph. The data is paragraphs before and after the target paragraph. We generated word embeddings of five targets word with conventional and our proposed methods, We compare existing and our proposed method for verification. We evaluate the inter-cluster and intra-claster variance and conduct the overall evaluation.