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Volume 40, Number 4, 2001
Knowledge Management
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

Text analysis and knowledge mining system - References
by T. Nasukawa and T. Nagano

Cited references and notes

  1. O. Zamir, O. Etzioni, and R. Karp, “Fast and Intuitive Clustering of Web Documents,” Proceedings of KDD-97 (1997), pp. 287–290.
  2. W. Cohen and H. Hirsh, “Joins That Generalize: Text Classification Using WHIRL,” Proceedings of KDD-98 (1998), pp. 169–173.
  3. G. Salton and M. J. McGill, SMART and SIRE Experimental Retrieval Systems, McGraw-Hill, Inc., New York (1983).
  4. A. M. Hearst, “Untangling Text Data Mining,” Proceedings of ACL-99 (1999), pp. 3–10.
  5. K. Night, “Mining Online Text,” Communications of the ACM 42, No. 11, 58–61 (1999).
  6. U. Hahn and K. Schnattinger, “Deep Knowledge Discovery from Natural Language Texts,” Proceedings of KDD-97 (1997), pp. 175–178.
  7. Information Extraction, Lecture Notes in Artificial Intelligence, M. T. Pazienza, Editor, Springer-Verlag, Rome (1997).
  8. Message Understanding Conferences, see http://www.itl.nist.gov/iad/894.02/related_projects/muc/index.html.
  9. R. Feldman and I. Dagan, “Knowledge Discovery in Textual Databases,” Proceedings of KDD-95 (1995), pp. 112–117.
  10. R. Feldman, W. Kloesgen, and A. Zilberstein, “Visualization Techniques to Explore Data Mining Results for Documents,” Proceedings of KDD-97 (1997), pp. 16–23.
  11. B. Lent, R. Agrawal, and R. Srikant, “Discovering Trends in Text Databases,” Proceedings of KDD-97 (1997), pp. 227–230.
  12. J. Mladenic, “Text-Learning and Related Intelligent Agents: A Survey,” IEEE Intelligent Systems 14, No. 4, 44–54 (1999).
  13. V. Hatzivassiloglou and K. McKeown, “Predicting the Semantic Orientation of Adjectives,” Proceedings of ACL-97 (1997), pp. 174–181.
  14. H. Matsuzawa and T. Fukuda, “Mining Structured Association Patterns from Databases,” Proceedings of the 4th Pacific and Asia International Conference on Knowledge Discovery and Data Mining (2000), pp. 233–244.
  15. The category of “fail” is very dependent on the domain. Thus, it should be defined in the semantic dictionary.
  16. R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proceedings of the ACM SIGMOD '93 (1993), pp. 207–216.
  17. H. Nomiyama, Topic Analysis in Newspaper Articles, Technical Report TR-0129, IBM Tokyo Research Laboratory, Tokyo (1996).
  18. M. Morohashi, K. Takeda, H. Nomiyama, and H. Maruyama, “Information Outlining—Filling the Gap Between Visualization and Navigation in Digital Libraries,” Proceedings of the International Symposium on Digital Libraries (1995), pp. 151–158.
  19. P. Xia, “Knowledge Discovery in Integrated Call Centers: A Framework for Effective Customer-Driven Marketing,” Proceedings of KDD-97 (1997), pp. 279–282.
  20. This category is contained in structured data, whereas calls on VoiceType were collected based on information in unstructured text.
  21. The verb “use” is “tsukau” in Japanese.
  22. Information on Medline can be found at http://www.nlm.nih.gov/.
  23. H. Maruyama, A Formal Approach to Japanese Analysis in Japanese-to-English Machine Translation, Dissertation, Kyoto University, Kyoto, Japan (1995).
  24. This is a result of analysis in a small set of sample data to demonstrate the capability of the system.
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