NDLI: Towards Concept-Based Translation Models Using Search Logs for Query Expansion

Content Provider	ACM Digital Library
Author	Gao, Jianfeng Nie, Jian-Yun
Abstract	Query logs have been successfully used to improve Web search. One of the directions exploits user clickthrough data to extract related terms to a query to perform query expansion (QE). How-ever, term relations have been created between isolated terms without considering their context, giving rise to the problem of term ambiguity. To solve this problem, we propose several ways to place terms in their contexts. On the one hand, contiguous terms can form a phrase; and on the other hand, terms at proximi-ty can provide less strict but useful contextual constraints mutual-ly. Relations extracted between such more constrained groups of terms are expected to be less noisy than those between single terms. In this paper, the constrained groups of terms are called concepts. We exploit user query logs to build statistical translation models between concepts, which are then used for QE. We perform experiments on the Web search task using a real world data set. Results show that the concept-based statistical translation model trained on clickthrough data outperforms signif-icantly other state-of-the-art QE systems.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2530275
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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Towards Concept-Based Translation Models Using Search Logs for Query Expansion