Japanese word sense disambiguation system based on deep feature extraction

The features of word sense disambiguation (WSD) come from the context. Japanese has linguistic features of both Chinese and English at the same time, thus the feature extraction of Japanese is more complicated. Considering Japanese features, based on the proposed WSD logic model and applying the characteristics of information integration of the maximum entropy model, WSD was solved by the deep feature extraction method, introducing semantics and syntactics features. Meanwhile, for preventing the skewed assignment of lonely word sense, the word sense tagging of word sequences was completed with the BeamSearch algorithm. Experiment results show that compared with WSD methods which only focus on the surface lexical features, the disambiguation accuracy of the Japanese WSD system proposed in this paper increases 2% to 3%, and the WSD accuracy of verbs improves 5%.