Browse wiki

Jump to: navigation, search
Learning to expand queries using entities
Abstract A substantial fraction of web search queriA substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of highquality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms accordingto their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.roach for entity-oriented query expansion.
Abstractsub A substantial fraction of web search queriA substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of highquality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms accordingto their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.roach for entity-oriented query expansion.
Bibtextype article  +
Doi 10.1002/asi.23084  +
Has author Brandao W.C. + , Santos R.L.T. + , Ziviani N. + , De Moura E.S. + , Da Silva A.S. +
Has extra keyword Feedback documents + , Learning to rank + , Pseudo relevance feedback + , State-of-the-art approach + , Supervised learning approaches + , Text retrieval conferences + , Web search queries + , Wikipedia articles +
Issn 23301635  +
Issue 9  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1870–1883  +
Published in Journal of the Association for Information Science and Technology +
Title Learning to expand queries using entities +
Type journal article  +
Volume 65  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 7 November 2014 19:06:26  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 19:06:26  +
DateThis property is a special property in this wiki. 2014  +
hide properties that link here 
Learning to expand queries using entities + Title
 

 

Enter the name of the page to start browsing from.