Distributional Models and Lexical Semantics in Convolution Kernels

  • Danilo Croce
  • Simone Filice
  • Roberto Basili
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7181)


The representation of word meaning in texts is a central problem in Computational Linguistics. Geometrical models represent lexical semantic information in terms of the basic co-occurrences that words establish each other in large-scale text collections. As recent works already address, the definition of methods able to express the meaning of phrases or sentences as operations on lexical representations is a complex problem, and a still largely open issue. In this paper, a perspective centered on Convolution Kernels is discussed and the formulation of a Partial Tree Kernel that integrates syntactic information and lexical generalization is studied. The interaction of such information and the role of different geometrical models is investigated on the question classification task where the state-of-the-art result is achieved.


Singular Value Decomposition Parse Tree Convolution Kernel Lexical Information Lexical Semantic 
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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Danilo Croce
    • 1
  • Simone Filice
    • 1
  • Roberto Basili
    • 1
  1. 1.Department of Enterprise EngineeringUniversity of Roma, Tor VergataRomaItaly

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