Learning to rank effective paraphrases from query logs for community question answering

Alejandro Figueroa, Günter Neumann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Citations (Scopus)

Abstract

We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Yahoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of queries and questions using the query-question click history. Elements of this corpus are automatically ranked according to recall and mean reciprocal rank, and then used for learning two independent learning to rank models (SVMRank), whereby a set of new query paraphrases can be scored according to recall and MRR. We perform several automatic evaluation procedures using cross-validation for analyzing the behavior of various aspects of our learned ranking functions, which show that our method is useful and effective for search in cQA.

Original languageEnglish
Title of host publicationProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Pages1099-1105
Number of pages7
Publication statusPublished - 2013
Event27th AAAI Conference on Artificial Intelligence, AAAI 2013 - Bellevue, WA, United States
Duration: 14 Jul 201318 Jul 2013

Publication series

NameProceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013

Other

Other27th AAAI Conference on Artificial Intelligence, AAAI 2013
Country/TerritoryUnited States
CityBellevue, WA
Period14/07/1318/07/13

ASJC Scopus subject areas

  • Artificial Intelligence

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