Language Modeling for Effective Construction of Domain Specific Thesauri

  • Libo Chen
  • Ulrich Thiel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3136)


In this paper we present an approach for effective construction of domain specific thesauri. We assume that the collection is partitioned into document categories. By taking advantage of these pre-defined categories, we are able to conceptualize a new topical language model to weight term topicality more accurately. With the help of information theory, interesting relationships among thesaurus elements are discovered deductively. Based on the “Layer-Seeds” clustering algorithm, topical terms from documents in a certain category will be organized according to their relationships in a tree-like hierarchical structure — a thesaurus. Experimental results show that the thesaurus contains satisfactory structures, although it differs to some extent from a manually created thesaurus. A first evaluation of the thesaurus in a query expansion task yields evidence that an increase of recall can be achieved without loss of precision.


Language Model Query Expansion Term Weighting Original Query Pointwise Mutual Information 
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.
    Crouch, C.J., Yang, B.: Experiments in automatic statistical thesaurus construction. In: SIGIR 1992, 15th Int. ACM/SIGIR Conf. on R&D in Information Retrieval, Copenhagen, Denmark, June 1992, pp. 77–87 (1992)Google Scholar
  2. 2.
    Fuhr, N., Roelleke, T.: HySpirit — A probabilistic inference engine for hypermedia retrieval in large databases. In: International Conference on Extending Database Technology, Valencia, Spain (1998)Google Scholar
  3. 3.
    Gelbukh, A., Sidorov, G., Guzman-Arenas, A.: Use of a weighted topic hierarchy for document classification. In: Matoušek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds.) TSD 1999. LNCS (LNAI), vol. 1692, p. 133. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  4. 4.
    Jing, Y.F., Croft, W.B.: An Association Thesaurus for Information Retrieval. In: RIAO 94 Conference Proceedings, New York, October 1994, pp. 146–160 (1994)Google Scholar
  5. 5.
    Lawrie, D.: Language Models for Hierarchical Summarization. Dissertation. University of Massachusetts, Amherst (2003)Google Scholar
  6. 6.
    Qiu, Y., Frei, H.P.: Concept based query expansion. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 160–170. ACM Press, New York (1993)Google Scholar
  7. 7.
    Salton, G.: Automatic Information Organization and Retrieval. McGraw-Hill Book Company, New York (1968)Google Scholar
  8. 8.
    Sanderson, M., Croft, B.: Deriving concept hierarchies from text. In: The Proceedings of the 22nd ACM SIGIR Conference, pp. 206–213 (1999)Google Scholar
  9. 9.
    Sparck-Jones, K.: Automatic Keyword Classification for Information Retrieval. Butterworth, London (1971)Google Scholar
  10. 10.
    Thiel, U., L’Abbate, M., Paradiso, A., Stein, A., Semeraro, G., Abbattista, F., Lops, P.: The COGITO Project. In: e-Business applications: results of applied research on e-Commerce, Supply Chain Management and Extended Enterprises. Section 2: eCommerce, Springer, Heidelberg (2002)Google Scholar
  11. 11.
    Kilgariff, A.: Thesauruses for Natural Language Processing. Technical Report Series: ITRI- 03-15, ITRI, Univ. of BrightonGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Libo Chen
    • 1
  • Ulrich Thiel
    • 1
  1. 1.Fraunhofer IPSIDarmstadtGermany

Personalised recommendations