Using the Structure of a Conceptual Network in Computing Semantic Relatedness

  • Iryna Gurevych
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3651)


We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitions proper written by humans and are processed by a dictionary based metric of semantic relatedness [1]. We implemented the metric on the basis of GermaNet, the German counterpart of WordNet, and evaluated the results on a German dataset of 57 word pairs rated by human subjects for their semantic relatedness. Our approach can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences.


Semantic Similarity Word Pair Semantic Relatedness Human Judgment Word Sense 
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.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, Toronto, Ontario, Canada, pp. 24–26 (June 1986)Google Scholar
  2. 2.
    Hirst, G., Budanitsky, A.: Correcting real-word spelling errors by restoring lexical cohesion. Natural Language Engineering 11(1), 87–111 (2005)CrossRefGoogle Scholar
  3. 3.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet:Similarity –Measuring the relatedness of concepts. In: Intelligent Systems Demonstrations of the Nineteenth National Conference on Artificial Intelligence (AAAI-2004), San Jose, CA, July 25–29 (2004)Google Scholar
  4. 4.
    Rubenstein, H., Goodenough, J.: Contextual Correlates of Synonymy. Communications of the ACM 8(10), 627–633 (1965)CrossRefGoogle Scholar
  5. 5.
    Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1–28 (1991)CrossRefGoogle Scholar
  6. 6.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 265–283. MIT Press, Cambridge (1998)Google Scholar
  7. 7.
    Seco, N., Veale, T., Hayes, J.: An Intrinsic Information Content Metric for Semantic Similarity in WordNet. In: Proceedings of the 16th European Conference on Artificial Intelligence, Valencia, Spain, August 22–27, pp. 1089–1090 (2004)Google Scholar
  8. 8.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montréal, Canada, August 20-25, vol. 1, pp. 448–453 (1995)Google Scholar
  9. 9.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the 10th International Conference on Research in Computational Linguistics (ROCLING), Tapei, Taiwan (1997)Google Scholar
  10. 10.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, San Francisco, Cal., pp. 296–304 (1998)Google Scholar
  11. 11.
    Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic relatedness for word sense disambiguation. In: Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, Mexico, pp. 241–257 (2003)Google Scholar
  12. 12.
    Ekedahl, J., Golub, K.: Word Sense Disambiguation using WordNet and the Lesk algorithm (2004),
  13. 13.
    Vaknin, S.: The definition of definitions (2005),
  14. 14.
    Kunze, C.: Lexikalisch-semantische Wortnetze. In: Carstensen, K.-U., Ebert, C., Endriss, C., Jekat, S., Klabunde, R., Langer, H. (eds.) Computerlinguistik und Sprachtechnologie. Eine Einführung, pp. 423–431. Spektrum Akademischer Verlag, Heidelberg (2004)Google Scholar
  15. 15.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  16. 16.
    Kunze, C., Lemnitzer, L.: GermaNet - representation, visualization, application. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC), Las Palmas, Canary Islands, Spain, May 29 - 31, pp. 1485–1491 (2002)Google Scholar
  17. 17.
    Banerjee, S., Pedersen, T.: Extended gloss overlap as a measure of semantic relatedness. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, August 28 – September 3 (1993)Google Scholar
  18. 18.
    Kilgarriff, A., Grefenstette, G.: Introduction to the special issue on the Web as a corpus. Computational Linguistics 29(3), 333–348 (2003)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Iryna Gurevych
    • 1
  1. 1.EML Research gGmbHHeidelbergGermany

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