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Identifying Authoritative and Reliable Contents in Community Question Answering with Domain Knowledge

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7867))

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Abstract

Community Question Answering (CQA) has emerged as a popular forum for users to ask and answer questions. Over the last few years, CQA portals such as Yahoo answersand Baidu Zhidao have exploded in popularity, and now provide a viable alternative to general purpose Web search. A number of answers submitted to address questions on CQA sites compose a valuable knowledge repository, which could be a gold mine for information retrieval as well as text mining. Two important questions in CQA research are focused on the quality of contents and the reputation of the answerers. Previous approaches for retrieving relevant and high quality content have been proposed, but not much work has been done on providing an integrated framework to solve these two problems. Besides, no research work has used both text and link information in their methods via leveraging existing ratings of answers and questions. In this paper, we present a novel approach to analyze questions and answers based on the topic modeling framework with Dirichlet forest priors (LDA-DF)[8]. We utilize information obtained from LDA-DF to construct a joint topical and link model to identify authorities and reliable answers on a CQA site.We evaluate our methods in a dataset obtained from Yahoo! Answers. With the new representation of topical structures on CQA datasets, using a limited amount of web resource, we show significant improvements over the state-of-art methods LDA-DF, LDA, and HLDA on performance of authority identification and answer ranking.

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Authors and Affiliations

  1. College of Information Science and Technology, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Lifan Guo & Xiaohua Hu

Authors
  1. Lifan Guo
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  2. Xiaohua Hu
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Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, 1 Mawson Lakes Boulevard, 5095, Adelaide, SA, Australia

    Jiuyong Li

  2. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, 2008, Sydney, NSW, Australia

    Longbing Cao  & Can Wang  & 

  3. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  4. School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Panyu District, 510006, Guangzhou, China

    Bo Liu

  5. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  6. Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, University Road, 701, Tainan, Taiwan

    Vincent S. Tseng

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Guo, L., Hu, X. (2013). Identifying Authoritative and Reliable Contents in Community Question Answering with Domain Knowledge. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-40319-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40318-7

  • Online ISBN: 978-3-642-40319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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