Advertisement

A Framework of NLP Based Information Tracking and Related Knowledge Organizing with Topic Maps

  • Asanee Kawtrakul
  • Chaiyakorn Yingsaeree
  • Frederic Andres
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4592)

Abstract

This paper presents a computational framework for information extraction and aggregation which aims to integrate and organize the data/information resources that spread throughout the Internet in the manner that makes them useful for tracking events such as natural disaster, and disease dispersion. We introduce a simple statistical information extraction technique for summarizing the document into a predefined structure. We apply the topic maps approach as a semantic layer in aggregating and organizing the extracted information for smart access. In addition, this paper also carries out a case study on disease dispersion domain using the proposed framework.

Keywords

Avian Influenza Information Extraction Relation Extraction Spatial Visualization Knowledge Repository 
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.

References

  1. 1.
    Kongwan, A., Kawtrakul, A.: Know-what: A Development of Object Property Extraction from Thai Texts and Query System. In: Proceedings of SNLP-2005. Bangkok, Thailand, pp. 157–162 (2005) ISBN: 974-9942-52-3 Google Scholar
  2. 2.
    Chanlekha, H., Kawtrakul, A.: Thai Named Entity Extraction by incorporating Maximum Entropy Model with Simple Heuristic Information. In: Proceedings of IJCNLP-2004, Hainan, China, pp. 49–55 (2004)Google Scholar
  3. 3.
    Sudprasert, S., Kawtrakul, A.: Thai Word Segmentation based on Global and Local Unsupervised Learning. In: Proceedings of NCSEC-2003, Chonburi, Thailand (2003)Google Scholar
  4. 4.
    Satayamas, V., Thumkanon, C., Kawtrakul, A.: Bootstrap Cleaning and Quality Control for Thai Tree Bank Construction. In: Proceedings of NCSEC-2005, Bangkok, Thailand pp. 849–860 ( 2005)Google Scholar
  5. 5.
    Grosz, B., Joshi, A., Weinstein, S.: Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics 21, 203–225 (1995)Google Scholar
  6. 6.
    Chareonsuk, J., Sukvakree, Y., Kawtrakul, A.: Elementary Discourse unit Segmentation for Thai using Discourse Cue and Syntactic Information. In: Proceedings of SNLP, Bangkok, Thailand, pp. 85–90 (2005)Google Scholar
  7. 7.
    Kawtrakul, A., Suktarachan, M., Imsombut, A.: Automatic Thai Ontology Construction and Maintenance System. In: Proceedings of Ontolex Workshop on LREC, pp. 68–74 (2004)Google Scholar
  8. 8.
    Biezunski, M., Bryan, M., Newcomb, S.: ISO/IEC JTC1/SC34 (May 22, 2002), available at http://www1.y12.doe.gov/capabilities/sgml/sc34/document/0322.htm
  9. 9.
    Kawtrakul, A., Imsombut, A., Thunyakijjanukit, A., Soergel, D., Liang, A., Sini, M., Johannsen, G., Keizer, K.: Automatic Term Relationship Cleaning and Refinement for AGROVOC. In: Proceedings of EFITA/WCCA, Vila Real, Portugal (2005)Google Scholar
  10. 10.
    Rajbhandari, S., Andres, F., Naito, M., Wuwongse, V.: Topic Management in Spatial-Temporal Multimedia Blog. In: the 1st IEEE International Conference on Digital Information Management (ICDIM 2006) Bangalore, India, December 6-8, 2006, pp. 81–88 (2006)Google Scholar
  11. 11.
    Thamvijit, D., Chanlekha, H., Sirigayon, C., Permpool, T., Kawtrakul, A.: Know-who: Person Information from Web Mining. In: Proceedings of NCSEC, Bangkok, Thailand, pp. 849–860 (2005)Google Scholar
  12. 12.
    Kawtrakul, A., Yingsaeree, C.: A Unified Framework for Automatic Metadata Extraction from Electronic Document. In: Proceedings of The International Advanced Digital Library Conference, Nagoya, Japan (2005)Google Scholar
  13. 13.
    Berger, A.L., Pietra, S.-A.D., Pietra, V.-J.D.: A maximum entropy approach to natural language processing. Computational Linguistics 22, 39–71 (1996)Google Scholar
  14. 14.
    Dean, M., Connolly, D., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., Stein, L.A.: OWL Web Ontology Language Reference. W3C Recommendation (February 10, 2004), available at http://www.w3.org/TR/owl-ref/
  15. 15.
    Garshol, L.M.: Living with Topic Maps and RDF (May 2003), available at http://www.idealliance.org/papers/dx_xmle03/papers/02-03-06/02-03-06.html
  16. 16.
    Yangarber, R., Jokipii, L., Rauramo, A., Huttunen, S.: Information Extraction from Epidemiological Reports. In: Proceedings of HLT/EMNLP-2005. Canada (2005)Google Scholar
  17. 17.
    Damianos, L., Bayer, S., Chisholm, M.A., Henderson, J., Hirschman, L., Morgan, W., Ubaldino, M., Zarrella, J.: MiTAP for SARS detection. In: Proceedings of the Conference on Human Language Technology, Boston, USA, pp. 241–244 (2004)Google Scholar
  18. 18.
    Naito, M., Andres, F.: Application Framework Based on Topic Maps. In: Maicher, L., Park, J. (eds.) TMRA 2005. LNCS (LNAI), vol. 3873, pp. 3–540. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Suzuki, J., Sasaki, Y., Maeda, E.: Kernels for structured natural language data. In: Proceeding of NIPS 2003 (2003)Google Scholar
  20. 20.
    Zavrel, J., Berck, P., Lavrijssen, W.: Information extraction by text classification: Corpus mining for features. In: Proceedings of the workshop Information Extraction meets Corpus Linguistics, Athens, Greece (2000)Google Scholar
  21. 21.
    Kushmerick, N., Johnston, E., McGuinness, S.: Information extraction by text classification. In: Proceedings of IJCAI-2001 Workshop on Adaptive Text Extraction and Mining (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Asanee Kawtrakul
    • 1
  • Chaiyakorn Yingsaeree
    • 1
  • Frederic Andres
    • 2
  1. 1.NAiST Research Laboratory, Kasetsart University, BangkokThailand
  2. 2.National Institute of Informatics, TokyoJapan

Personalised recommendations