Improving question recommendation by exploiting information need

Shuguang Li, Suresh Manandhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we address the problem of question recommendation from large archives of community question answering data by exploiting the users' information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommendation. We show that translation model can be effectively utilized to predict the information need given only the user's query question. Experiments show that the proposed information need prediction approach can improve the performance of question recommendation.
Original languageEnglish
Title of host publicationProceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
Place of PublicationStroudsburg, PA, USA
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Print)978-1-932432-87-9
Publication statusPublished - 2011

Publication series

NameHLT '11
PublisherAssociation for Computational Linguistics

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