Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Boyd-graber, J., Fellbaum, C., Osherson, D., Schapire, R.: Adding dense, weighted connections to wordnet. In: Proceedings of the Third International WordNet Conference (2006)
Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)
Cilibrasi, R.L., Vitanyi, P.M.B.: The google similarity distance. IEEE Trans. on Knowl. and Data Eng. 19(3), 370–383 (2007)
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)
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)
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)
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)
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)
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)
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)
Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1), 1–28 (1991)
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)
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)
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)
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)
Rubenstein, H., Goodenough, J.B.: Contextual correlates of synonymy. Commun. ACM 8(10) (October 1965)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferrara, F., Tasso, C. (2013). Evaluating the Results of Methods for Computing Semantic Relatedness. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37247-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-37247-6_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37246-9
Online ISBN: 978-3-642-37247-6
eBook Packages: Computer ScienceComputer Science (R0)