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Classifying questions into fine-grained categories using topic enriching

Lu, Z and Lin, YR and Zhang, Q and Chen, M (2016) Classifying questions into fine-grained categories using topic enriching. In: UNSPECIFIED.

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Abstract

The lasting popularity of many social Q&A websites, such as Yahoo! Answers and ResearchGate, has become valuable knowledge repositories for people to search for answers to questions in various aspects in life. Finding the most relevant questions is often a non-Trivial task, and a fine-grained classification system of questions will be an important aid. Existing work mainly focused on classifying questions into different major categories (e.g., "Health","Computer", etc.) without further dealing with the fine-grained categories (e.g., "Dental","Skin Conditions", etc.). Identifying questions' finegrained categories is challenging due to the limited length of a question and insufficient content information available in these social Q&A websites. In this work, we propose a novel framework to classify questions into fine-grained categories based on enriching the related topics of questions. We leverage word embedding feature representation with topic modelings to determine the extended feature terms, i.e., terms that do not appeared in original question content. The enriched features then are used for fine-grained category classification. Extensive experiment results based on three large data collections showcase the effectiveness of our proposed approach.

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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lu, Z
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Zhang, Q
Chen, M
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
CorrespondentLin, Yu-Ruyurulin@pitt.eduYURULINUNSPECIFIED
Date: 1 January 2016
Date Type: Publication
Journal or Publication Title: Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016
Page Range: 166 - 174
Event Type: Conference
DOI or Unique Handle: 10.1109/iri.2016.29
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781509032075
Date Deposited: 27 Jun 2017 15:02
Last Modified: 30 Mar 2021 10:55
URI: http://d-scholarship.pitt.edu/id/eprint/32567

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