Learning phrase-based spelling error models from clickthrough data

Learning phrase-based spelling error models from clickthrough data (2010)

by Xu Sun , Daniel Micol , Jianfeng Gao , Chris Quirk

Venue:	In ACL
Citations:	11 - 2 self

Datum	Value	Source
TITLE	Learning phrase-based spelling error models from clickthrough data	INFERENCE
AUTHOR NAME	Xu Sun	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Mathematical Informatics; University of Tokyo	SVM HeaderParse 0.2
AUTHOR ADDR	Tokyo, Japan	SVM HeaderParse 0.2
AUTHOR NAME	Daniel Micol	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Corporation	SVM HeaderParse 0.2
AUTHOR ADDR	Munich, Germany	SVM HeaderParse 0.2
AUTHOR NAME	Jianfeng Gao	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA, USA	SVM HeaderParse 0.2
AUTHOR NAME	Chris Quirk	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA, USA	SVM HeaderParse 0.2
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. 1	SVM HeaderParse 0.2
YEAR	2010	INFERENCE
VENUE	In ACL	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	22 found	ParsCit 1.0