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Modelling word meaning using efficient tensor representations

Symonds, Michael, Bruza, Peter D., Sitbon, Laurianne, & Turner, Ian (2011) Modelling word meaning using efficient tensor representations. In Proceedings of 25th Pacific Asia Conference on Language, Information and Computation, Nanyang Technological University, Singapore.

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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.

Impact and interest:

5 citations in Scopus
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221 since deposited on 12 Oct 2011
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ID Code: 46419
Item Type: Conference Paper
Keywords: Semantic Space, Tensors, unsupervised learning, linguistics, tensor encoding
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Cognitive Science not elsewhere classified (170299)
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Information Systems
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2011 Mike Symonds, Peter Bruza, Laurianne Sitbon, and Ian Turner
Deposited On: 13 Oct 2011 09:50
Last Modified: 27 Oct 2013 15:24

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