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Mining Specific Features for Acquiring User Information Needs

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Advances in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7818))

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Abstract

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

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Author information

Authors and Affiliations

  1. College of Computer Science, King Khalid University, Saudi Arabia, B.O.Box 394, ABHA, 61411

    Abdulmohsen Algarni

  2. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    Yuefeng Li

Authors
  1. Abdulmohsen Algarni
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  2. Yuefeng Li
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Editor information

Editors and Affiliations

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

    Jian Pei

  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao  & Guandong Xu  & 

  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan

    Hiroshi Motoda

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Algarni, A., Li, Y. (2013). Mining Specific Features for Acquiring User Information Needs. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_44

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  • DOI: https://doi.org/10.1007/978-3-642-37453-1_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37452-4

  • Online ISBN: 978-3-642-37453-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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