Automatic Extraction of Semantic Relations by Using Web Statistical Information

  • Valeria Borzì
  • Simone Faro
  • Arianna Pavone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8577)


A semantic network is a graph which represents semantic relations between concepts, used in a lot of fields as a form of knowledge representation. This paper describes an automatic approach to identify semantic relations between concepts by using statistical information extracted from the Web. We automatically constructed an associative network starting from a lexicon. Moreover we applied these measures to the ESL semantic similarity test proving that our model is suitable for representing semantic correlations between terms obtaining an accuracy which is comparable with the state of the art.


Semantic Similarity Semantic Relatedness Semantic Network Automatic Extraction Computational Linguistics 
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.
    Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pasca, M., Soroa, A.: A study on similarity and relatedness using distributional and wordnet-based approaches. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Boulder, pp. 19–27 (June 2009)Google Scholar
  2. 2.
    Collins, A., Loftus, E.: A spreading activation theory of semantic processing. Psychological Review 82, 407–428 (1975)CrossRefGoogle Scholar
  3. 3.
    Fellbaum, C. (ed.): WordNet: An Electronic Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  4. 4.
    Francis, W.N., Kucera, H.: Frequency Analysis of English Usage: Lexicon and Grammar. Houghton Mifflin (1982)Google Scholar
  5. 5.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence (IJCAI 2007), Hyderabad, pp. 1606–1611 (January 2007)Google Scholar
  6. 6.
    Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. WordNet An electronic lexical database, 305–332 (April 1998)Google Scholar
  7. 7.
    Hughes, T., Ramage, D.: Lexical semantic relatedness with random graph walks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing - Conference on Computational Natural Language Learning (EMNLP-CoNLL), Prague, pp. 581–589 (June 2007)Google Scholar
  8. 8.
    Janetzko, D.: Objectivity, Reliability, and Validity of Search Engine Count Estimates. International Journal of Internet Science 3(1), 7–33 (2008)Google Scholar
  9. 9.
    Jarmasz, M., Szpakowicz, S.: Roget’s thesaurus and semantic similarity. In: Proceedings of Recent Advances in Natural Language Processing, Borovets, pp. 212–219 (September 2003)Google Scholar
  10. 10.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference on Research in Computational Linguistics, Taipei, Taiwan, pp. 19–33 (August 1997)Google Scholar
  11. 11.
    Leacock, C., Chodorow, M.: Combining local context and wordnet similarity for word sense identification. WordNet: An Electronic Lexical Database 49(2), 265–283 (1998)Google Scholar
  12. 12.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, Madison, vol. 1, pp. 296–304 (July 1998)Google Scholar
  13. 13.
    Navigli, R.: Word Sense Disambiguation: A survey. ACM Computing Surveys 41 (2009)Google Scholar
  14. 14.
    Navigli, R., Ponzetto, S.: BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network. Artificial Intelligence 193 (2012)Google Scholar
  15. 15.
    Ross Quillian, M.: Semantic memory. In: Minsky, M. (ed.) Semantic Information Processing. MIT Press, Cambridge (1968)Google Scholar
  16. 16.
    Rayson, P., Charles, O., Auty, I.: Can Google count? Estimating search engine result consistency. In: Proceedings of the Seventh Web as Corpus Workshop, pp. 23–30 (2012)Google Scholar
  17. 17.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: International Joint Conference for Artificial Intelligence, Montreal, pp. 448–453 (August 1995)Google Scholar
  18. 18.
    Siblini, R., Kosseim, L.: Using a Weighted Semantic Network for Lexical Semantic Relatedness. In: Proceedings of Recent Advances in Natural Language Processing (RANLP 2013), Hissar, Bulgaria (September 2013)Google Scholar
  19. 19.
    Strube, M., Ponzetto, S.P.: WikiRelate! Computing semantic relatedness using Wikipedia. In: Proceedings of the National Conference on Artificial Intelligence, Boston, vol. 21, p. 1419 (July 2006)Google Scholar
  20. 20.
    Terra, E., Clarke, C.L.A.: Frequency estimates for statistical word similarity measures. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Edmonton, vol. 21, pp. 165–172 (May 2003)Google Scholar
  21. 21.
    Tsatsaronis, G., Varlamis, I., Vazirgiannis, M.: Text relatedness based on a word thesaurus. Journal of Artificial Intelligence Research 37(1), 1–40 (2010)zbMATHGoogle Scholar
  22. 22.
    Turney, P.D.: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491–502. Springer, Heidelberg (2001)Google Scholar
  23. 23.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, New Mexico, pp. 133–138 (June 1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Valeria Borzì
    • 1
  • Simone Faro
    • 1
  • Arianna Pavone
    • 2
  1. 1.Dipartimento di Matematica e InformaticaUniversità di CataniaCataniaItaly
  2. 2.Dipartimento di Scienze UmanisticheUniversità di CataniaCataniaItaly

Personalised recommendations