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Modelling word meaning using efficient tensor representations

By Michael Symonds, Peter D. Bruza, Laurianne Sitbon and Ian Turner


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.\ud \ud 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

Topics: 080200 COMPUTATION THEORY AND MATHEMATICS, 170299 Cognitive Science not elsewhere classified, Semantic Space, Tensors, unsupervised learning, linguistics, tensor encoding
Year: 2011
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