In most previous research on distribu- tional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntac- tic/semantic types of words (e.g., depen- dency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representa- tions to build VSM for the task of seman- tic composition of adjective-noun phrases. Through extensive experiments on bench- mark datasets, we find that even though a type-based VSM is effective for seman- tic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector represen- tation of a phrase fro...

Click Here to View the Paper In XML Format