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Space Projections as Distributional Models for Semantic Composition

  • Paolo Annesi
  • Valerio Storch
  • Roberto Basili
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7181)

Abstract

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.

Keywords

Target Word Space Projection Word Pair Latent Semantic Analysis Vector Space Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paolo Annesi
    • 1
  • Valerio Storch
    • 1
  • Roberto Basili
    • 1
  1. 1.Department of Enterprise EngineeringUniversity of Roma Tor VergataRomaItaly

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