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Identifying semantic equivalence for multi-document summarisation
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Identifying semantic equivalence for multi-document summarisation
Eamonn Newman1  , Joe Carthy1, John Dunnion1 and Nicola Stokes2
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School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland |
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NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, Melbourne, Australia |
Published online: 22 August 2007
Abstract We describe Semantic Equivalence and Textual Entailment Recognition, and outline a system which uses a number of lexical,
syntactic and semantic features to classify pairs of sentences as “semantically equivalent”. We describe an experiment to
show how syntactic and semantic features improve the performance of an earlier system, which used only lexical features. We
also outline some areas for future work.
Keywords Semantic equivalence - Textual entailment - Document summarisation - NLP
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