Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation

Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a
particular context. Although recent studies have demonstrated some progress in the advancement of neural
language models, the scope of research is still such that the senses of several words can only be determined
in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can
address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that
is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the
meaning of words. We propose a new WSD model that individually determines the sense of the word in
accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method
that separately classies the less frequently used senses for achieving a reasonable performance. We have
conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches.