Extracting Term Collocations for Directing Users to Informative Web Pages

  • Eiko Yamamoto
  • Hitoshi Isahara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)


Due to the rapid increase in the number of existing web pages, accessing the pertinent information we seek has become more difficult, especially when one cannot hit upon the proper keywords for the search engine. When we encounter such a situation, we often try to add more keywords to the previous query. However, adding the appropriate words so as to extract only useful documents is rather difficult. In order to solve this problem, we developed a method of extracting term collocations that could help limit the extracted pages to those that are more informative. In order to verify the usefulness of our approach, we extracted collocations from web documents within the medical domain as an example, and we input those collocations into a search engine and retrieved information from the Internet. The results verified that the collocations extracted by this methodology directed users to more informative web pages.


Patent Ductus Arteriosus Necrotizing Enterocolitis Mefenamic Acid MeSH Tree Subject Noun 
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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  1. 1.
    Hagita, N., Sawaki, M.: Robust recognition of degraded machine-printed characters using complementary similarity measure and error-correction learning. In: Proceedings of the SPIE – The International Society for Optical Engineering, vol. 2442, pp. 236–244 (1995)Google Scholar
  2. 2.
    Kanzaki, K., Yamamoto, E., Ma, Q., Isahara, H.: Construction of an objective hierarchy of abstract concepts via directional similarity. In: Proceedings of the 20th International Conference on Computational Linguistics, vol. 2, pp. 1147–1153 (2004)Google Scholar
  3. 3.
    Manning, D.C., Schutze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)MATHGoogle Scholar
  4. 4.
    Nakagawa, H., Mori, T.: A simple but powerful automatic term extraction method. In: Proceedings of the 2nd International Workshop on Computational Terminology, pp. 29–35 (2002)Google Scholar
  5. 5.
    Yamamoto, E., Kanzaki, K., Isahara, H.: Extraction of hierarchies based on inclusion of co-occurring words with frequency information. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 1166–1172 (2005)Google Scholar
  6. 6.
    Yamamoto, E., Umemura, K.: A similarity measure for estimation of one–to-many relationship in corpus. Journal of Natural Language Processing 9, 45–75 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eiko Yamamoto
    • 1
  • Hitoshi Isahara
    • 1
  1. 1.National Institute of Information and Communications TechnologyKyotoJapan

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