Learning phrase-based spelling error models from clickthrough data

  • Authors:
  • Xu Sun;Jianfeng Gao;Daniel Micol;Chris Quirk

  • Affiliations:
  • University of Tokyo, Tokyo, Japan;Microsoft Research, Redmond, WA;Microsoft Corporation, Munich, Germany;Microsoft Research, Redmond, WA

  • Venue:
  • ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
  • Year:
  • 2010

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Abstract

This paper explores the use of clickthrough data for query spelling correction. First, large amounts of query-correction pairs are derived by analyzing users' query reformulation behavior encoded in the clickthrough data. Then, a phrase-based error model that accounts for the transformation probability between multi-term phrases is trained and integrated into a query speller system. Experiments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms significantly its baseline systems.