Modelling Word Meaning using Efficient Tensor Representations
QUT ePrints (Queensland University of Technology)Published 1 December 2011Open access
Michael Symonds, Peter Bruza, Laurianne Sitbon, Ian Turner
Citations11
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TL;DR
This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.
Abstract
Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these models has been raised as a weakness when performing cognitive tasks. This paper presents an efficient tensor based approach to modelling word meaning that builds on recent attempts to encode word order information, while providing flexible methods for extracting task specific semantic information.
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