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Semantic Textual Similarity Using Various Approaches

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Machine Intelligence and Big Data in Industry

Part of the book series: Studies in Big Data ((SBD,volume 19))

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Abstract

The paper is devoted to the semantic textual similarity (STS) problem. Given two sentences of text, s1 and s2, the systems participating in this problem should compute how similar s1 and s2 are, returning a similarity score. We present our experience in this topic, ranging from the knowledge-poor approaches to some compact and easy applied knowledge-rich methods (using structured knowledge base frameworks like WordNet, Wikipedia or BabelNet). The evaluation of the proposed methods was performed using the datasets from SemEval-2014/15 tasks.

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References

  1. http://en.wikipedia.org/wiki/Cosine_similarity

  2. http://lucene.apache.org/

  3. Kozłowski, M.: OPI: Semeval-2014 task 3 system description. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 454–458. Dublin, Ireland, August 23–24 (2014)

    Google Scholar 

  4. Navigli, R., Ponzetto, S.: Multilingual WSD with just a few lines of code: The BabelNet API. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 67–72. Jeju, Republic of Korea (2012)

    Google Scholar 

  5. Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)

    Article  MATH  Google Scholar 

  6. Hughes, T., Ramage, D.: Lexical semantic relatedness with random graph walk. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 581–589 (2007)

    Google Scholar 

  7. Islam, A., Inkpen, D.: Semantic text similarity using corpus-based word similarity and string similarity. ACM Trans. Knowl. Discovery Data 2(2), 1–25 (2008)

    Article  Google Scholar 

  8. Landauer, T., Dumais, S.: A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104, 211–240 (1997)

    Article  Google Scholar 

  9. Landauer, T., McNamara, D., Dennis, S., Kintsch, W.: Handbook of Latent Semantic Analysis. Psychology Press (2007)

    Google Scholar 

  10. Leacock, C., Chodorow, M.: Combining local context and WordNet sense similarity for word sense identification. In: WordNet, An Electronic Lexical Database, pp. 265–283 (1998)

    Google Scholar 

  11. Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In: Proceedings of the SIGDOC Conference, pp. 24–26 (1986)

    Google Scholar 

  12. Li, Y., McLean, D., Bandar, Z., O’Shea, J., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18(8), 1138–1149 (2006)

    Article  Google Scholar 

  13. Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and Knowledge-based measures of text semantic similarity. In: Proceedings of the American Association for Artificial Intelligence, pp. 775–780 (2006)

    Google Scholar 

  14. Pilehvar, M., 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 

  15. Salton, G., Lesk, M.: Computer Evaluation of Indexing and Text Processing. Prentice-Hall, Englewood Cliffs, New Jersey (1971)

    MATH  Google Scholar 

  16. Salton, G., McGill, M.: Alternation. In: Introduction to Modern Information Retrieval, McGraw-Hill, New York (1983)

    Google Scholar 

  17. Turney, P., Pantel, P.: From frequency to meaning: Vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010)

    MathSciNet  MATH  Google Scholar 

  18. Turney, P.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In: Proceedings of the Twelfth European Conference on Machine Learning, pp. 491–502 (2001)

    Google Scholar 

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Correspondence to Maciej Kazuła .

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Kazuła, M., Kozłowski, M. (2016). Semantic Textual Similarity Using Various Approaches. In: Ryżko, D., Gawrysiak, P., Kryszkiewicz, M., Rybiński, H. (eds) Machine Intelligence and Big Data in Industry. Studies in Big Data, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-30315-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-30315-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30314-7

  • Online ISBN: 978-3-319-30315-4

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