Evaluating the Results of Methods for Computing Semantic Relatedness

  • Felice Ferrara
  • Carlo Tasso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7816)


The semantic relatedness between two concepts is a measure that quantifies the extent to which two concepts are semantically related. Due to the growing interest of researchers in areas such as Semantic Web, Information Retrieval and NLP, various approaches have been proposed in the literature for automatically computing the semantic relatedness. However, despite the growing number of proposed approaches, there are still significant criticalities in evaluating the results returned by different semantic relatedness methods. The limitations of the state of the art evaluation mechanisms prevent an effective evaluation and several works in the literature emphasize that the exploited approaches are rather inconsistent. In this paper we describe the limitations of the mechanisms used for evaluating the results of semantic relatedness methods. By taking into account these limitations, we propose a new methodology and new resources for comparing in an effective way different semantic relatedness approaches.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A.: Semeval-2012 task 6: A pilot on semantic textual similarity. In: *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Proceedings of the Sixth International Workshop on Semantic Evaluation, June 7-8, pp. 385–393. Association for Computational Linguistics, Montréal (2012)Google Scholar
  2. 2.
    Boyd-graber, J., Fellbaum, C., Osherson, D., Schapire, R.: Adding dense, weighted connections to wordnet. In: Proceedings of the Third International WordNet Conference (2006)Google Scholar
  3. 3.
    Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)MATHCrossRefGoogle Scholar
  4. 4.
    Cilibrasi, R.L., Vitanyi, P.M.B.: The google similarity distance. IEEE Trans. on Knowl. and Data Eng. 19(3), 370–383 (2007)CrossRefGoogle Scholar
  5. 5.
    Ferrara, F., Tasso, C.: Integrating semantic relatedness in a collaborative filtering system. In: Proceedings of the 19th Int. Workshop on Personalization and Recommendation on the Web and Beyond, pp. 75–82 (2012)Google Scholar
  6. 6.
    Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. ACM Trans. Inf. Syst. 20(1), 116–131 (2002)CrossRefGoogle Scholar
  7. 7.
    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, pp. 1606–1611. Morgan Kaufmann Publishers Inc., San Francisco (2007)Google Scholar
  8. 8.
    Gracia, J.L., Mena, E.: Web-Based Measure of Semantic Relatedness. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 136–150. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Gurevych, I.: Using the Structure of a Conceptual Network in Computing Semantic Relatedness. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 767–778. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Hayes, J., Veale, T., Seco, N.: Enriching wordnet via generative metonymy and creative polysemy. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation, pp. 149–152. European Language Resources Association (2004)Google Scholar
  11. 11.
    Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, ACL 1998, vol. 2, pp. 768–774. Association for Computational Linguistics, Stroudsburg (1998)Google Scholar
  12. 12.
    Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1–28 (1991)CrossRefGoogle Scholar
  13. 13.
    Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: an Evolving Synergy, pp. 25–30. AAAI Press (2008)Google Scholar
  14. 14.
    Nikolova, S., Boyd-Graber, J., Fellbaum, C.: Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools. In: Mehler, A., Kühnberger, K.-U., Lobin, H., Lüngen, H., Storrer, A., Witt, A. (eds.) Modeling, Learning, and Proc. of Text-Tech. Data Struct. SCI, vol. 370, pp. 81–93. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Pedersen, T., Pakhomov, S.V.S., Patwardhan, S., Chute, C.G.: Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics 40(3), 288–299 (2007)CrossRefGoogle Scholar
  16. 16.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 1, pp. 448–453. Morgan Kaufmann Publishers Inc., San Francisco (1995)Google Scholar
  17. 17.
    Rubenstein, H., Goodenough, J.B.: Contextual correlates of synonymy. Commun. ACM 8(10) (October 1965)Google Scholar
  18. 18.
    Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using wikipedia. In: Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006, vol. 2, pp. 1419–1424. AAAI Press (2006)Google Scholar
  19. 19.
    Zesch, T., Gurevych, I.: Automatically creating datasets for measures of semantic relatedness. In: Proceedings of the Workshop on Linguistic Distances, LD 2006, pp. 16–24. Association for Computational Linguistics, Stroudsburg (2006)CrossRefGoogle Scholar
  20. 20.
    Zesch, T., Gurevych, I.: The more the better? assessing the influence of wikipedia’s growth on semantic relatedness measures. In: Chair, N.C.C., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) Proceedings of the Seventh International Conference on Language Resources and Evaluation. European Language Resources Association, Valletta (2010)Google Scholar
  21. 21.
    Zesch, T., Gurevych, I.: Wisdom of crowds versus wisdom of linguists; measuring the semantic relatedness of words. Nat. Lang. Eng. 16(1), 25–59 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Felice Ferrara
    • 1
  • Carlo Tasso
    • 1
  1. 1.Artificial Intelligence Lab., Department of Mathematics and Computer ScienceUniversity of UdineItaly

Personalised recommendations