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.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Xu Sun, Jianfeng Gao, Daniel Micol, Chris Quirk
• Keywords: ACL 2010, Clickthrough Data, Computational Linguistics, Phrase-based Error Model, Query Reformulation Behavior