Advertisement

Semantic Similarity Between Adjectives and Adverbs—The Introduction of a New Measure

  • Moreno ColomboEmail author
  • Edy Portmann
Chapter
  • 4 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 899)

Abstract

It is believed that language is the window into the mind, thus a fundamental building brick for the development of artificial intelligence. Understanding natural language is therefore a crucial task for computer systems of the future, and as such it is widely studied. An important tool for the decoding of language are semantic similarity measures, or the description of closeness of the meaning of words. A lot of research on semantic similarity on a conceptual level exists, comparing thus objects and their interconnections, which is typical of similarity between nouns. In this article we propose a novel way of exploring spectral similarity, or the closeness of adjectives and adverbs with respect to the spectrum of all words describing the same feature (e.g., the closeness of sometimes and seldom with respect to the spectrum of words going from never to always). The proposed semantic similarity measure is based on overlaps in second order synonyms of words, and its accuracy is validated in this article thanks to a comparison with human assessment of similarity between words. With a custom accuracy estimate taking in account the fuzziness of the proposed similarity measure, an accuracy of the algorithm of about \(96\%\) is obtained.

Notes

Acknowledgements

We thank Jhonny Pincay Nieves and Minh Tue Nguyen for their precious contribution in the review of the data used for the creation of the questionnaire as well as the refinement of the survey itself, and Sara D’Onofrio for her valuable revision of this article. We moreover express our gratitude to all the participants to the survey for their fundamental contribution to the evaluation of our algorithm.

References

  1. 1.
    Ruder, S.: Neural Transfer Learning for Natural Language Processing (2019)Google Scholar
  2. 2.
    Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41 (2009).  https://doi.org/10.1145/1459352.1459355
  3. 3.
    Budanitsky, A., Hirst, G.: Semantic distance in WordNet: an experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and Other Lexical Resources 2 (2001)Google Scholar
  4. 4.
    D’Onofrio, S., Müller, S.M., Papageorgiou, E.I., Portmann, E.: Fuzzy reasoning in cognitive cities: an exploratory work on fuzzy analogical reasoning using fuzzy cognitive maps. In: 2018 IEEE International Conference on Fuzzy Systems, pp. 1–8 (2018).  https://doi.org/10.1109/FUZZ-IEEE.2018.8491474
  5. 5.
    Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 103–111 (2006).  https://doi.org/10.1109/91.493904CrossRefGoogle Scholar
  6. 6.
    Gupta, C., Jain, A., Joshi, N.: Fuzzy logic in natural language processing - a closer view. Procedia Comput. Sci. 132, 1375–1384 (2018).  https://doi.org/10.1016/j.procs.2018.05.052CrossRefGoogle Scholar
  7. 7.
    Zhao, L., Ichise, R., Mita, S., Sasaki, Y.: An ontology-based intelligent speed adaptation system for autonomous cars. In: The 4th Joint International Semantic Technology Conference (2014).  https://doi.org/10.1007/978-3-319-15615-6_30
  8. 8.
    Turney, P.D.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In: 2th European Conference on Machine Learning (2001).  https://doi.org/10.1007/3-540-44795-4_42
  9. 9.
    Bollegala, D., Matsuo, Y., Ishizuka, M.: Measuring semantic similarity between words using web search engines. Proc. WWW 2017(7), 757–766 (2007)Google Scholar
  10. 10.
    Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th international conference on World Wide Web, pp. 377–386 (2006)Google Scholar
  11. 11.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  12. 12.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)CrossRefGoogle Scholar
  13. 13.
    Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  14. 14.
    Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. WordNet: An electronic lexical database, pp. 305–332 (1998)Google Scholar
  15. 15.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. An electronic lexical database, WordNet (1998)Google Scholar
  16. 16.
    Pilehvar, M.T., Jurgens, D., Navigli, R.: Align, disambiguate and walk: a unified approach for measuring semantic similarity. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1341–1351 (2013)Google Scholar
  17. 17.
    Pilehvar, M.T., Navigli, R.: From senses to texts: an all-in-one graph-based approach for measuring semantic similarity. Artif. Intell. 228, 95–128 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: International conference on intelligent text processing and computational linguistics, pp. 136–145 (2002)Google Scholar
  19. 19.
    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, pp. 24–26 (1986)Google Scholar
  20. 20.
    Finkelstein, L., et al.: Placing search in context: the concept revisited. ACM Trans. Inf. Syst. 20, 116–131 (2002)CrossRefGoogle Scholar
  21. 21.
    Rubenstein, H., Goodenough, J.B.: Contextual correlates of synonymy. Commun. ACM 8, 627–633 (1965)CrossRefGoogle Scholar
  22. 22.
    Marková, V.: Synonyme unter dem Mikroskop. Eine korpuslinguistische Studie. Korpuslinguistik und interdisziplinäre Perspektiven auf Sprache 2 (2012)Google Scholar
  23. 23.
    Kendall, M.: A new measure of rank correlation. Biometrika 30, 81–89 (1938).  https://doi.org/10.1093/biomet/30.1-2.81CrossRefzbMATHGoogle Scholar
  24. 24.
    Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications. J. Am. Stat. Assoc. 49, 732–764 (1954).  https://doi.org/10.2307/2281536CrossRefzbMATHGoogle Scholar
  25. 25.
    Spearman, C.: Proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)CrossRefGoogle Scholar
  26. 26.
    Somers, R.H.: A new asymmetric measure of association for ordinal variables. Am. Soc. Rev. 27 (1962).  https://doi.org/10.2307/2090408
  27. 27.
    Kumar, R., Vassilvitskii, S.: Generalized distances between rankings. In: Proceedings of the 19th international conference on World wide web, pp. 571–580 (2010).  https://doi.org/10.1145/1772690.1772749
  28. 28.
    Müller, S., D’Onofrio, S., Portmann, E.: Fuzzy analogical reasoning in cognitive cities - a conceptual framework for urban dialogue systems. In: Proceedings of the 20th International Conference on Enterprise Information Systems, vol. 1, pp. 353–360 (2018)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Human-IST InstituteUniversity of FribourgFribourgSwitzerland

Personalised recommendations