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Algebraic Model to Formalize the Sentences and Their Context for Texts Written in the Spanish Language

  • Edgardo Samuel Barraza VerdesotoEmail author
  • Edwin Rivas TrujilloEmail author
  • Víctor Hugo Medina GarcíaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 959)

Abstract

This paper introduces a model based on set theory and modern algebra that formalizes sentences and their context. The model aims at dividing sentences into cores which will be mapped into sets of an algebraic space; some of these cores have a type of context called strictly linguistic context. These sets along with an operation form Abelian groups. In addition, the model defines a function that can completely (or partially) restore the original sentence from such sets while guaranteeing its structure and meaning. This could be accomplished through queries that compare contexts and activate the task of restoring sentences. The use case scenario has been limited to the Spanish language. All these processes can be applied in many scenarios, but our focus lies in the dynamic creation of small theories.

Keywords

Algebraic structure Abelian group Context Nominal phrases Verbal cores Small theories 

References

  1. 1.
    Kecskes, I.: The paradox of communications. Socio-cognitive approach to pragmatics. Inf. Control., 52–55 (2010)Google Scholar
  2. 2.
    Tulving, E.: Episodic and semantic memory. In: Tulving, E., Donaldson, W. (eds.) Organization of Memory, pp. 381–403. Academic Press, New York (1972)Google Scholar
  3. 3.
    Havel, I.M.: Strategies of Remembrance: From Pindar to Hölderlin, chap. 2. Cambridge Scholars (2009)Google Scholar
  4. 4.
    Conway, M., Pleydell-Pearce, C.: The construction of autobiographical memories in the self memory system. Psychol. Rev. 107(2), 261–288 (2000)CrossRefGoogle Scholar
  5. 5.
    Bazire, M., Brézillon, P.: Understanding Context before using it. In: Dey, A., Kokinov, B., Leake, D., Turner, R. (eds.) CONTEXT 2005. LNCS (LNAI), vol. 3554, pp. 29–40. Springer, Heidelberg (2005).  https://doi.org/10.1007/11508373_3CrossRefzbMATHGoogle Scholar
  6. 6.
    Buvac, S., Mason, I.A.: Propositional logic of context. In: AAAI, pp. 412–419 (1993)Google Scholar
  7. 7.
    McCarthy, J.: Notes on formalizing context. In: Proceedings of the 13th International Joint Conference on Artifical Intelligence, San Francisco, CA, USA, pp. 555–560 (1993)Google Scholar
  8. 8.
    Guha, R.: Contexts: a formalization and some applications (1992)Google Scholar
  9. 9.
    Ghidini, C., Giunchiglia, F.: Local models semantics, or contextual reasoning=locality+compatibility. Artif. Intell. 127(2), 221–259 (2001).  https://doi.org/10.1016/S0004-3702(01)00064-9MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Haase, P., et al.: D3.1.1 context languages - state of the art. Technical report D3.1.1, Universität Karlsruhe (TH) (2006)Google Scholar
  11. 11.
    Costanza, P., Hirschfeld, R.: Language constructs for context-oriented programming: an overview of contextl. In: Proceedings of the 2005 Symposium on Dynamic Languages, New York, NY, USA, pp. 1–10 (2005)Google Scholar
  12. 12.
    Salvaneschi, G., Ghezzi, C., Pradella, M.: Context-oriented programming: A software engineering perspective. J. Syst. Softw. 85(8), 1801–1817 (2012).  https://doi.org/10.1016/j.jss.2012.03.024CrossRefGoogle Scholar
  13. 13.
    Luna Traill, E., Vigueras Avila, A., Baez Pinal, G.E.: Diccionario básico de lingüística. UNAM (2005)Google Scholar
  14. 14.
    Vivaldi, G.M., Sánchez, A.: Curso de Redacción. Thomson Eds, Paraninfo S.A. (2000)Google Scholar
  15. 15.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995).  https://doi.org/10.1006/ijhc.1995.1081CrossRefGoogle Scholar
  16. 16.
    Cimiano, P., Völker, J.: Text2onto - a framework for ontology learning and data-driven change discovery. In: Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB), Alicante, Spain, pp. 227–238 (2005)Google Scholar
  17. 17.
    Velardi, P., Faralli, S., Navigli, R.: Ontolearn reloaded: a graph-based algorithm for taxonomy induction. Comput. Linguist. 39(3), 665–707 (2013). http://dblp.uni-trier.de/db/journals/coling/coling39.html#VelardiFN13CrossRefGoogle Scholar
  18. 18.
    Lenat, D.B., Guha, R.V.: Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)Google Scholar
  19. 19.
    Arroyo Cantón, C., Berlato Rodríguez, P.: La comunicación. Lengua castellana y Literatura. Oxford University Press, España (2012)Google Scholar
  20. 20.
    Montealegre, R.: La comprension del texto: Sentido y significado. Revista Latinoamericana de Psicología. Universidad Nacional de Colombia 36(2), 243–255 (2004)Google Scholar
  21. 21.
    González Calvo, J.M.: Los conceptos de proposición, oración y enunciado. La frase nominal. Liceus Servicios de Gestión y Comunicación S.L (2006)Google Scholar
  22. 22.
    Bick, E.: A constraint grammar-based parser for spanish. In: TIL (2006)Google Scholar
  23. 23.
    Boeree, G.: Basic language structures (2017). http://webspace.ship.edu/cgboer/basiclangstruct.html

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of SevilleSevilleSpain
  2. 2.University Distrital Francisco José de CaldasBogotáColombia

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