Abstract
Word2vec is a tool that produces a vector representation of words from a large amount of text data. In this paper, we show that only a part of the vector space produced by word2vec is enough to represent the collective sense of a set of related words in the Japanese WordNet. Further, we will show that there is a subspace in the vector space which do not relate to the collective sense. We construct a compact decision tree by using the vectors in order to distinguish whether a given word belongs to the set of related words.
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