Here, we explore the use of low-dimensional word vectors learned by word2vec from the large corpus: the meaning of a pattern is represented by the distribution of entity vectors.
we obtain the vector representation of a relational pattern p, p = Here, v e 0 denotes the vector for an entity e 0 in the entity pair e, and v e 1 does the vector for another entity e 1 in the pair e. For our experimental corpus, we collected 15 billion Japanese sentences by crawling web pages.