Surrounding Word Sense Model for Japanese All-words Word Sense Disambiguation

This paper proposes a surrounding word sense model (SWSM) that uses the distribution of word senses that appear near ambiguous words for unsupervised all-words word sense disambiguation in Japanese. Although it was inspired by the topic model, ambiguous Japanese words tend to have similar topics since coarse semantic polysemy is less likely to occur than that in Western languages as Japanese uses Chinese characters, which are ideograms. We thus propose a model that uses the distribution of word senses that appear near ambiguous words: SWSM. We embedded the concept dictionary of an Electronic Dictionary Research (EDR) electronic dictionary in the system and used the Japanese Corpus of EDR for the experiments, which demonstrated that SWSM outperformed a system with a random baseline and a system that used a topic model called Dirichlet Allocation with WORDNET (LDAWN), especially when there were high levels of entropy for the word sense distribution of ambiguous words.