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A Rule Based Approach to Message Board Topics Classification

  • Fabrizio Antonelli
  • Maria Luisa Sapino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3665)

Abstract

The importance of web discussion boards is growing with the interest of sharing knowledge and doubts with colleagues in a working/studying environment. The challenge is to organize the structure of discussion boards, to make the navigation easier, and to effectively extract relevant information. Message hierarchies in web discussion boards, manually organised by users participating into the discussion, might grow uncontrolled, thus making navigation more and more difficult for users. The goal of this paper is to develop a technique to organise messages in a message board, by automatically classifying and annotating pairs of postings to guide users through discussion segments relevant to their navigational goals.

Keywords

Entry Point Rule Base System Discussion Board Message Board Text Mining Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [AK04]
    Aasheim, C., Koehler, G.J.: Mining Message Board Content on the World Wide Web for Organizational Information. In: Barko, C., Nemati, H. (eds.) Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance, pp. 188–200. Idea Group, Inc., USA (2004)Google Scholar
  2. [CL01]
    Candan, K.S., Li, W.-S.: Reasoning for web document associations and its applications in site map construction. Int. Journal of Data and Knowledge Engineering (2002)Google Scholar
  3. [Cohen96]
    Cohen, W.W.: Learning rules that classify e-mail. In: AAAI Spring Symposium on Machine Learning in Information Access, pp. 18–25 (1996)Google Scholar
  4. [CKP04]
    Crawford, E., Koprinska, I., Patrick, J.: Phrases and Feature Selec-tion in E-Mail Classification. In: The Proceedings of the Australasian Document Computing Symposium (2004)Google Scholar
  5. [KCD05]
    Kim, J.W., Candan, K.S., Donderler, M.E.: Topic Segmentation of Message Hierarchies for Indexing and Navigation Support. In: WWW 2005, Japan (2005)Google Scholar
  6. [LKVT00]
    Li, W.-S., Kolak, O., Vu, Q., Takano, H.: Defining Logical Domains in a Web Site. In: Proceedings of the 2000 ACM Hypertext Conference, San Antonio, Texas, USA (May 2000)Google Scholar
  7. [LCN03]
    Liu, B., Chin, C.W., Ng, H.T.: Mining topic-specific concepts and definitions on the web. In: Proceedings of the twelfth international conference on World Wide Web, WWW 2003, Budapest, Hungary, May 20-24 (2003)Google Scholar
  8. [MG98]
    Mladenic, D., Grobelnik, M.: Feature selection for classification based on text hierarchy. In: Working notes of Learning from Text and the Web, Conference on Automated Learning and Discovery CONALD 1998, Pittsburg, USA (1998)Google Scholar
  9. [MTN01]
    Murakami, A., Takeda, K., Nagao, K.: Discussion Mining:Knowledge Discovery from Online Discussion Records. In: NLPRS Workshop XML and NLP (2001)Google Scholar
  10. [YP97]
    Yang, P.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning table of con-tents, pp. 412–420 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Fabrizio Antonelli
    • 1
  • Maria Luisa Sapino
    • 1
  1. 1.Università degli Studi di Torino 

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