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A Comparative Study of Two Short Text Semantic Similarity Measures

  • James O’Shea
  • Zuhair Bandar
  • Keeley Crockett
  • David McLean
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4953)

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 self-service, 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.

Keywords

Natural Language Semantic Similarity Dialogue Management User Modeling Benchmark Sentence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • James O’Shea
    • 1
  • Zuhair Bandar
    • 1
  • Keeley Crockett
    • 1
  • David McLean
    • 1
  1. 1.Department of Computing and MathematicsManchester Metropolitan UniversityManchesterUnited Kingdom

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