Clickthrough-Based Translation Models for Web Search: from Word Models to Phrase Models

Clickthrough-Based Translation Models for Web Search: from Word Models to Phrase Models

Cached

Download Links

by Jianfeng Gao , Xiaodong He , Jian-yun Nie

Citations:

7 - 4 self

Versions

Version 0

Version History

Metadata Version 0

Datum	Value	Source
TITLE	Clickthrough-Based Translation Models for Web Search: from Word Models to Phrase Models	SVM HeaderParse 0.2
AUTHOR NAME	Jianfeng Gao	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft Way	SVM HeaderParse 0.2
AUTHOR ADDR	Redmond, WA 98052 USA	SVM HeaderParse 0.2
AUTHOR NAME	Xiaodong He	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052 USA	SVM HeaderParse 0.2
AUTHOR NAME	Jian-yun Nie	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Montreal	SVM HeaderParse 0.2
AUTHOR ADDR	CP. 6128, succursale Centre-ville; Montreal, Quebec H3C 3J7 Canada	SVM HeaderParse 0.2
ABSTRACT	Web search is challenging partly due to the fact that search queries and Web documents use different language styles and vocabularies. This paper provides a quantitative analysis of the language discrepancy issue, and explores the use of clickthrough data to bridge documents and queries. We assume that a query is parallel to the titles of documents clicked on for that query. Two translation models are trained and integrated into retrieval models: A word-based translation model that learns the translation probability between single words, and a phrase-based translation model that learns the translation probability between multi-term phrases. Experiments are carried out on a real world data set. The results show that the retrieval systems that use the translation models outperform significantly the systems that do not. The paper also demonstrates that standard statistical machine translation techniques such as word alignment, bilingual phrase extraction, and phrase-based decoding, can be adapted for building a better Web document retrieval system.	SVM HeaderParse 0.2
CITATIONS	37 found	ParsCit 1.0