Lemon and Tea Are Not Similar: Measuring Word-to-Word Similarity by Combining Different Methods

  • Rajendra BanjadeEmail author
  • Nabin Maharjan
  • Nobal B. Niraula
  • Vasile Rus
  • Dipesh Gautam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)


Substantial amount of work has been done on measuring word-to-word relatedness which is also commonly referred as similarity. Though relatedness and similarity are closely related, they are not the same as illustrated by the words lemon and tea which are related but not similar. The relatedness takes into account a broader ranLemge of relations while similarity only considers subsumption relations to assess how two objects are similar. We present in this paper a method for measuring the semantic similarity of words as a combination of various techniques including knowledge-based and corpus-based methods that capture different aspects of similarity. Our corpus based method exploits state-of-the-art word representations. We performed experiments with a recently published significantly large dataset called Simlex-999 and achieved a significantly better correlation (ρ = 0.642, P < 0.001) with human judgment compared to the individual performance.


Similarity Relatedness Word-to-Word Similarity 


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  1. 1.
    Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Paşca, M., Soroa, A.: A study on similarity and relatedness using distributional and WordNet-based approaches. In: Proceedings of HLT: The Annual Conference of NAACL, pp. 19–27. Association for Computational Linguistics (2009)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Burgess, C., Lund, K.: Hyperspace analog to language (hal): A general model of semantic representation. In: Proceedings of the Annual Meeting of the Psychonomic Society, vol. 12, pp. 177–210 (1995)Google Scholar
  4. 4.
    Fellbaum, C.: WordNet. Blackwell Publishing Ltd. (1998)Google Scholar
  5. 5.
    Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: The concept revisited. In: Proceedings of the 10th International Conference on World Wide Web, pp. 406–414. ACM (2001)Google Scholar
  6. 6.
    Gabrilovich, E., Markovitch, S.: Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis. In: IJCAI, vol. 7, pp. 1606–1611 (2007)Google Scholar
  7. 7.
    Graesser, A.C., Penumatsa, P., Ventura, M., Cai, Z., Hu, X.: Using LSA in AutoTutor: Learning through mixed initiative dialogue in natural language. In: Handbook of Latent Semantic Analysis, pp. 243–262 (2007) Google Scholar
  8. 8.
    Han, L., Kashyap, A., Finin, T., Mayfield, J., Weese, J.: UMBC EBIQUITY-CORE: Semantic textual similarity systems. In: Proceedings of the Second Joint Conference on Lexical and Computational Semantics, vol. 1, pp. 44–52 (2013)Google Scholar
  9. 9.
    Hill, F., Reichart, R., Korhonen, A.: Simlex-999: Evaluating semantic models with (genuine) similarity estimation. arXiv preprint arXiv:1408.3456 (2014)Google Scholar
  10. 10.
    Hinton, G.E.: Distributed representations (1984)Google Scholar
  11. 11.
    Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. WordNet: An Electronic Lexical Database 305, 305–332 (1998)Google Scholar
  12. 12.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008Google Scholar
  13. 13.
    Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Processes 25(2-3), 259–284 (1998)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Lee, J.H., Kim, M.H., Lee, Y.J.: Information retrieval based on conceptual distance in is-a hierarchies. Journal of Documentation 42(2), 188–207 (1989)Google Scholar
  16. 16.
    Lin, D.: An information-theoretic definition of similarity. In: ICML, vol. 98, pp. 296–304 (1998)Google Scholar
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  18. 18.
    Mohler, M., Mihalcea, R.: Text-to-text semantic similarity for automatic short answer grading. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 567–575. Association for Computational Linguistics (March 2009)Google Scholar
  19. 19.
    Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic relatedness for word sense disambiguation. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 241–257. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  20. 20.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet:: Similarity: measuring the relatedness of concepts. In: Demonstration Papers at HLT-NAACL 2004, pp. 38–41. Association for Computational Linguistics (May 2004)Google Scholar
  21. 21.
    Pennington, J., Socher, R., Manning, C.: Glove: Global vectors for word representationGoogle Scholar
  22. 22.
    Potthast, M., Gollub, T., Hagen, M., Kiesel, J., Michel, M., Oberländer, A., Stein, B.: Overview of the 4th International Competition on Plagiarism Detection. In: CLEF (Online Working Notes/Labs/Workshop) (September 2012)Google Scholar
  23. 23.
    Resnik, P.: Using information content to evaluate semantic similarity in taxonomy. arXiv preprint cmp-lg/9511007 (1995) Google Scholar
  24. 24.
    Rus, V., Lintean, M.: A comparison of greedy and optimal assessment of natural language student input using word-to-word similarity metrics. In: Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pp. 157–162. Association for Computational Linguistics (2012)Google Scholar
  25. 25.
    Rus, V., Lintean, M.C., Banjade, R., Niraula, N. B., Stefanescu, D.: SEMILAR: The Semantic Similarity Toolkit. In: ACL (Conference System Demonstrations), pp. 163–168 (August 2013) Google Scholar
  26. 26.
    Rus, V., Niraula, N., Banjade, R.: Similarity measures based on latent dirichlet allocation. In: Gelbukh, A. (ed.) CICLing 2013, Part I. LNCS, vol. 7816, pp. 459–470. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  27. 27.
    Ştefănescu, D., Banjade, R. Rus, V.: Latent Semantic Analysis Models on Wikipedia and TASA, LREC (2014)Google Scholar
  28. 28.
    Stefanescu, D., Rus, V., Niraula, N.B., Banjade, R.: Combining Knowledge and Corpus-based Measures for Word-to-Word Similarity. In: The Twenty-Seventh International Flairs Conference (March 2014)Google Scholar
  29. 29.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394. Association for Computational Linguistics (July 2010)Google Scholar
  30. 30.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd Annual Meeting of the Association for Computational Linguistics, pp.133–138 (1994)Google Scholar
  31. 31.
    Niraula, N.B., Gautam, D., Banjade, R., Maharjan, N., Rus, V.: Combining Word Representations for Measuring Word Relatedness and Similarity. In: The Proceedings of 28th International FLAIRS Conference (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rajendra Banjade
    • 1
    Email author
  • Nabin Maharjan
    • 1
  • Nobal B. Niraula
    • 1
  • Vasile Rus
    • 1
  • Dipesh Gautam
    • 1
  1. 1.Department of Computer ScienceThe University of MemphisMemphisUSA

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