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Supervised Distributional Semantic Relatedness

  • Alistair Kennedy
  • Stan Szpakowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7499)

Abstract

Distributional measures of semantic relatedness determine word similarity based on how frequently a pair of words appear in the same contexts. A typical method is to construct a word-context matrix, then re-weight it using some measure of association, and finally take the vector distance as a measure of similarity. This has largely been an unsupervised process, but in recent years more work has been done devising methods of using known sets of synonyms to enhance relatedness measures. This paper examines and expands on one such measure, which learns a weighting of a word-context matrix by measuring associations between words appearing in a given context and sets of known synonyms. In doing so we propose a general method of learning weights for word-context matrices, and evaluate it on a word similarity task. This method works with a variety of measures of association and can be trained with synonyms from any resource.

Keywords

Training Data Semantic Relatedness Computational Linguistics Context Level Pointwise Mutual Information 
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

  • Alistair Kennedy
    • 1
  • Stan Szpakowicz
    • 1
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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