Linguistic Relations Encoding in a Symbolic- Connectionist Hybrid Natural Language Processor

  • João Luís Garcia Rosa
  • Edson Françozo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1952)


In recent years, the Natural Language Processing scene has witnessed the steady growth of interest in connectionist modeling. The main appeal of such an approach is that one does not have to determine the grammar rules in advance: the learning abilities displayed by such systems take care of input regularities. Better and faster learning can be obtained through the implementation of a symbolic-connectionist hybrid system. Such system combines the advantages of symbolic approaches, by introducing symbolic rules as network connection weights, with the advantages of connectionism. In a hybrid system called HTRP, words within a sentence are represented by means of semantic features. The features for the verbs are arranged along certain semantic dimensions, and are mutually exclusive within each dimension. One may infer that this happens because of the semantic features encoded in the network inputs.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dowty, D.: On the Semantic Content of the Notion of ‘Thematic Role’, in G. Chierchia, B. H. Partee, and R. Turner (Eds.), Properties, Types, and Meaning, vol. 2, Semantic Issues, Dordrecht: Kluwer (1989) 69–129Google Scholar
  2. 2.
    Franchi, C. and Cançado, M.: Thematic Hierarchy (in Portuguese), Unpublished paper, Unicamp/USP, UFMG, Brazil (1998)Google Scholar
  3. 3.
    Fu, L. M.: Knowledge-Based Connectionism for Revising Domain Theories, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No.1 (1993) 173–182CrossRefGoogle Scholar
  4. 4.
    Garcez, A. S. d’A., Zaverucha, G., and Carvalho, L. A. V.: Connectionist Inductive Learning and Logic Programming System, System Engineering and Computing Program —COPPE/UFRJ, Rio de Janeiro, Brazil, Technical Report (1997)Google Scholar
  5. 5.
    Haegeman, L.: Introduction to Government and Binding Theory. Cambridge: Blackwell (1991)Google Scholar
  6. 6.
    Lawrence, S., Giles, C. L., and Fong, S.: Natural Language Grammatical Inference with Recurrent Neural Networks. IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 1 (2000) 126–140CrossRefGoogle Scholar
  7. 7.
    McRae, K., Ferretti, T. R., and Amyote, L.: Thematic Roles as Verb-specific Concepts, Language and Cognitive Processes, 12 (2/3) (1997) 137–176CrossRefGoogle Scholar
  8. 8.
    McClelland, J. L. and Kawamoto, A. H.: Mechanisms of Sentence Processing: Assigning Roles to Constituents of Sentences. In J. L. McClelland, D. E. Rumelhart (Eds.), Parallel Distributed Processing, Volume 2. A Bradford Book, The MIT Press (1986)Google Scholar
  9. 9.
    Rosa, J. L. G. and Françozo, E.: Hybrid Thematic Role Processor: Symbolic Linguistic Relations Revised by Connectionist Learning. Proceedings of IJCAI’99 — Sixteenth International Joint Conference on Artificial Intelligence, Volume 2, Stockholm, Sweden, 31 July-6 August, Morgan Kaufmann (1999) 852–857Google Scholar
  10. 10.
    Rosenblatt, F.: The Perceptron: A Perceiving and Recognizing Automaton, Report 85-460-1, Project PARA, Cornell Aeronautical Laboratory, Ithaca, New York (1957)Google Scholar
  11. 11.
    Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning Internal Representations by Error Propagation, in D. E. Rumelhart and J. L. McClelland (Eds.), Parallel Distributed Processing-Volume 1: Foundations, A Bradford Book, The MIT Press (1986)Google Scholar
  12. 12.
    Setiono, R. and Liu, H.: Symbolic Representation of Neural Networks, IEEE Computer, Vol. 29, No. 3 (1996) 71–77Google Scholar
  13. 13.
    Towell, G. G. and Shavlik, J. W.: Extracting Refined Rules from Knowledge-based Neural Networks, Machine Learning, 13 (1993) 71–101Google Scholar
  14. 14.
    Waltz, D. L. and Pollack, J. B.: Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretations. Cognitive Science 9 (1985) 51–74CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • João Luís Garcia Rosa
    • 1
  • Edson Françozo
    • 2
  1. 1.Instituto de InformáticaCampinas - SPBrazil
  2. 2.LAFAPEInstituto de Estudos da LinguagemCampinas - SPBrazil

Personalised recommendations