Advertisement

Comparative Analysis and Implementation of Semantic-Based Classifiers

  • Luis Miguel Escobar-VegaEmail author
  • Víctor Hugo Zaldívar-CarrilloEmail author
  • Ivan Villalon-TurrubiatesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11289)

Abstract

Text classifiers that extract their features with pure statistical methods are not very useful when there is an extended range of types to classify. They also lack a deeper understanding of the classified data. The use of some semantic methods can improve the efficiency and effectiveness of the purely quantitative approach. This work explores the use of a semantic approach based on a similarity measure to build a vector model containing some semantic evidence. This vector model is used to improve a Maximum Entropy-based text classifier. Experiments show that the F-measures obtained using this approach are competitive. One may conclude that the use of semantic analysis is an excellent complement to statistical approaches and produces better performance and high-grade results.

Keywords

Organizational knowledge Knowledge management Semantic technology Semantic web Text classification 

References

  1. 1.
    Altszyler, E., Sigman, M., Ribeiro, S., Slezak, D.: Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database. Technical report. arXiv:1610.01520v2 [cs.CL], ArXiV, April 2017
  2. 2.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)Google Scholar
  3. 3.
    Cohen, S.: Bayesian Analysis in Natural Language Processing, 1st edn. Morgan and Claypool, Toronto (2016)Google Scholar
  4. 4.
    Elekes, A., Schäler, M., Boehm, K.: On the various semantics of similarity in word embedding models. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pp. 1–10. ACM/IEEE, June 2017Google Scholar
  5. 5.
    Finkel, J.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics, vol. 1, no. 1, pp. 363–370, July 2005Google Scholar
  6. 6.
    Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3(1), 1289–1305 (2003)zbMATHGoogle Scholar
  7. 7.
    GoodRelations: The most powerful Web vocabulary for e-commerce, 2 July 2018. http://wiki.goodrelations-vocabulary.org/Datasets
  8. 8.
  9. 9.
    GRIAL-Projects: SenSem: Databank of Spanish sentences annotated syntactically and semantically, 17 February 2018. http://grial.uab.es/fproj.php?id=1&idioma=in.
  10. 10.
    Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic Similarity from Natural Language and Ontology Analysis, 1st edn. Morgan and Claypool, Toronto (2017)Google Scholar
  11. 11.
    Harris, Z.: Distributional structure. Word 10(2), 146–162 (1954)Google Scholar
  12. 12.
    Jurafsky, D., James, M.: Speech and language processing, 3rd edn. Prentice-Hall, Upper Saddle River (2017)Google Scholar
  13. 13.
    Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, vol. 1, no. 37, pp. 957–966 (2015)Google Scholar
  14. 14.
    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(2), 211–240 (1997)Google Scholar
  15. 15.
    Landauer, T., Laham, D.: An introduction to latent semantic analysis. Discourse Process 25(1), 259–284 (1998)Google Scholar
  16. 16.
    Lenci, A.: Distributional approaches in linguistic and cognitive research. Ital. J. Linguist. 20(1), 1–31 (2008)Google Scholar
  17. 17.
    Lewis, D.D.: Naive (Bayes) at forty: the independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998).  https://doi.org/10.1007/BFb0026666Google Scholar
  18. 18.
    Li, Z., Ding, Q., Zhang, W.: A comparative study of different distances for similarity estimation. Intell. Comput. Inf. Sci. 134(1), 483–488 (2011)Google Scholar
  19. 19.
    Liu, B., Hsu, W., Ma, Y., Ma, B.: Integrating classification and association rule mining. In: Knowledge Discovery and Data Mining, vol. 32, no. 4, pp. 80–86 (1998)Google Scholar
  20. 20.
    Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. CoRR 1(1), 1–2, January 2013Google Scholar
  21. 21.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013 Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, no. 1, pp. 3111–3119, December 2013Google Scholar
  22. 22.
    Miller, G., Fellbaum, C.: Wordnet then and now. ECML 41(2), 209–214 (2007)Google Scholar
  23. 23.
    Mirończuk, M., Protasiewicz, J.: A recent overview of the state-of-the-art elements of text classification. Expert Syst. Appl. 106(1), 36–54 (2018)Google Scholar
  24. 24.
    Nigam, K., Lafferty, J., Mccallum, A.: Using maximum entropy for text classification. In: IJCAI 1999 Workshop on Machine Learning for Information Filtering, vol. 1, no. 1, pp. 61–67, August 1999Google Scholar
  25. 25.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543. Association for Computational Linguistics, November 2014Google Scholar
  26. 26.
    Pushp, P., Srivastava, M.: Train once, test anywhere: zero-shot learning for text classification. Technical reporty. arXiv:1612.03651 [cs.CL], ArXiV, Dic 2017. https://arxiv.org/abs/1712.05972
  27. 27.
    RAE: Real Academia Española (Jan 9 2018) http://www.rae.es
  28. 28.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Mag. Commun. ACM 18(11), 613–620 (1975)zbMATHGoogle Scholar
  29. 29.
    Scikit-learn: Scikit-learn Machine Learning in Python, 29 January 2018. http://scikit-learn.org/stable/
  30. 30.
    Séaghdha, D.: Semantic classification with Wordnet kernels. ECML 37(1), 237–240 (2015)Google Scholar
  31. 31.
    SemEval: Multilingual and Cross-lingual Semantic Word Similarity, 5 January 2018. http://alt.qcri.org/semeval2017/task2/index.php?id=task-details
  32. 32.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis, 1st edn. Cambridge, Cambridge (2004)zbMATHGoogle Scholar
  33. 33.
    Shevlyakov, G.: Robust Correlation: Theory and Applications, 1st edn. Wiley, West Sussex (2016)zbMATHGoogle Scholar
  34. 34.
    Sidorov, G., Gelbukh, A., Gómez-Adorno, H., Pinto, D.: Soft similarity and soft cosine measure: similarity of features in vector space model. Computacion Sistemas 18(3), 491–504 (2014)Google Scholar
  35. 35.
    Taule, M., Martí, A., Recasens, M.: Ancora: multilingual and multilevel annotated corpora. In: Proceedings of 6th International Conference on Language Resources and Evaluation, vol. 1, no. 1, pp. 96–101, January 2008Google Scholar
  36. 36.
    Tversky, A., Itamar, G.: Studies of similarity. Cogn. Categorization 84(4), 79–98 (1978)Google Scholar
  37. 37.
    UNSPCP: United Nations Standard Products and Services Code, 25 August 2017. https://www.unspsc.org
  38. 38.
    Yao, X.: Semantic conceptual primitives computing in text classification. In: NAACL Short, vol. 15, no. 3, pp. 66–70 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ITESO (Instituto Tecnológico y de Estudios Superiores de Occidente)TlaquepaqueMexico

Personalised recommendations