Space Projections as Distributional Models for Semantic Composition

Empirical distributional methods account for the meaning of syntactic structures by combining words according to algebraic operators (e.g. tensor product) acting over the corresponding lexical constituents. In this paper, a novel approach for semantic composition based on space projection techniques over the basic geometric lexical representations is proposed. In line with Frege’s context principle, the meaning of a phrase is modeled in terms of the subset of properties shared by the co-occurring words. In the geometric perspective here pursued, syntactic bi-grams are projected in the so called Support Subspace, aimed at emphasizing the semantic features shared by the compound words and better capturing phrase-specific aspects of the involved lexical meanings. State-of-the-art results are achieved in a well known phrase similarity task, used as a benchmark for this class of methods.