Paper: Learning Phrase-Based Spelling Error Models from Clickthrough Data

ACL ID P10-1028
Title Learning Phrase-Based Spelling Error Models from Clickthrough Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

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. Expe- riments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms signifi- cantly its baseline systems.