Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2011

Abstract

We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at query level than document level. We present two query weighting schemes. The first compresses the query into a query feature vector, which aggregates all document instances in the same query, and then conducts query weighting based on the query feature vector. This method can efficiently estimate query importance by compressing query data, but the potential risk is information loss resulted from the compression. The second measures the similarity between the source query and each target query, and then combines these fine-grained similarity values for its importance estimation. Adaptation experiments on LETOR3.0 data set demonstrate that query weighting significantly outperforms document instance weighting methods.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)

First Page

112

Last Page

122

Publisher

Association for Computational Linguistics

City or Country

Portland, OR, USA

Additional URL

https://aclweb.org/anthology/P11-1012

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