CCG Categories for Distributional Semantic Models

For the last decade, distributional semantics has been an active area of research to address the problem of understanding the semantics of words in natural language. The core principal of the distributional semantic approach is that the linguistic context surrounding a given word, which is represented as a vector, provides important information about its meaning. In this paper we investigate the possibility to exploit Combinatory Categorial Grammar (CCG) categories as syntactic features to be relevant for characterizing the context vector and hence the meaning of words. We find that the CCG categories can enhance the representation of verb meaning.