A tensor encoding model for semantic processing

Authors:
Michael Symonds;Peter D. Bruza;Laurianne Sitbon;Ian Turner
Affiliations:
Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.