Empirical distributional methods account for the meaning of syntactic structures by combining word vectors according to algebraic operators. In this paper, a novel approach for semantic composition based on space projection techniques over lexical vector representations is proposed. In line with the principle of compositionality, the meaning of a phrase is modeled in terms of the subset of properties shared by co-occurring words. Syntactic bi-grams are thus projected in the so called Support Subspace, corresponding to such properties. State-of-the-art results are achieved in a well known phrase similarity task, used as a benchmark for this class of methods. © 2012 Springer-Verlag.
CITATION STYLE
Annesi, P., Storch, V., & Basili, R. (2012). Space projections as distributional models for semantic composition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7181 LNCS, pp. 323–335). https://doi.org/10.1007/978-3-642-28604-9_27
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