A comparative study of two short text semantic similarity measures

  • Authors:
  • James O'Shea;Zuhair Bandar;Keeley Crockett;David McLean

  • Affiliations:
  • Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom

  • Venue:
  • KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
  • Year:
  • 2008

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Abstract

This paper describes a comparative study of STASIS and LSA. These measures of semantic similarity can be applied to short texts for use in Conversational Agents (CAs). CAs are computer programs that interact with humans through natural language dialogue. Business organizations have spent large sums of money in recent years developing them for online customer selfservice, but achievements have been limited to simple FAQ systems. We believe this is due to the labour-intensive process of scripting, which could be reduced radically by the use of short-text semantic similarity measures. "Short texts" are typically 10-20 words long but are not required to be grammatically correct sentences, for example spoken utterances and text messages. We also present a benchmark data set of 65 sentence pairs with human-derived similarity ratings. This data set is the first of its kind, specifically developed to evaluate such measures and we believe it will be valuable to future researchers.