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Learning Phrase-Based Spelling Error Models from Clickthrough Data

9 years 11 months ago
Learning Phrase-Based Spelling Error Models from Clickthrough Data
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.
Xu Sun, Jianfeng Gao, Daniel Micol, Chris Quirk
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ACL
Authors Xu Sun, Jianfeng Gao, Daniel Micol, Chris Quirk
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