Algebraic compositional models for semantic similarity in ranking and clustering

Although distributional models of word meaning have been widely used in Information Retrieval achieving an effective representation and generalization schema of words in isolation, the composition of words in phrases or sentences is still a challenging task. Different methods have been proposed to account on syntactic structures to combine words in term of algebraic operators (e.g. tensor product) among vectors that represent 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 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 benchmark for phrase similarity task and the generalization capability of the proposed operators is investigated in a cross-linguistic scenario, i.e. in the English and Italian Language.