Discussion Mining: Knowledge Discovery from Semantically Annotated Discussion Content

  • Katashi Nagao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3609)


We present discussion mining as a preliminary study of knowledge discovery from discussion content of offline meetings. Our system generates minutes for such meetings semi-automatically and links them with audio-visual data of discussion scenes. Then, not only retrieval of the discussion content, but also we are pursuing the method of searching for a similar discussion to an ongoing discussion from the past ones, and the method of generation of an answer to a certain question based on the accumulated discussion content. In terms of mailing lists and online discussion systems such as bulletin board systems, various studies have been done. However, what we think is greatly different from the previous works is that ours includes face-to-face offline meetings. We analyze meetings from diversified perspectives using audio and visual information. We also developed a tool for semantic annotation on discussion content. We consider this research not just data mining but a kind of real-world human activity mining.


Semantic Annotation Word Sense Disambiguation Video Annotation Scalable Vector Graphic Summarization Method 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    The Apache XML Project. Apache Xindice (2001),
  2. 2.
    Conklin, J., Begeman, M.L.: gIBIS: A Hypertext Tool for Exploratory Policy Discussion. In: Proc. of CSCW ’88, pp. 140–152 (1988)Google Scholar
  3. 3.
  4. 4.
    Nagao, K., Ohira, S., Yoneoka, M.: Annotation-Based Multimedia Annotation and Transcoding. In: Proceedings of the Nineteenth International Conference on Computational Linguistics (COLING 2002), pp. 702–708 (2002)Google Scholar
  5. 5.
    Nagao, K.: Digital Content Annotation and Transcoding. Artech House Publishers, Norwood (2003)Google Scholar
  6. 6.
    W3C. Scalable Vector Graphics (SVG) 1.0 Specification (2001),
  7. 7.
    W3C. The Semantic Web Community Portal (2002),

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Katashi Nagao
    • 1
  1. 1.EcoTopia Science Institute, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603Japan

Personalised recommendations