Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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) 69–129
Franchi, C. and Cançado, M.: Thematic Hierarchy (in Portuguese), Unpublished paper, Unicamp/USP, UFMG, Brazil (1998)
Fu, L. M.: Knowledge-Based Connectionism for Revising Domain Theories, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No.1 (1993) 173–182
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)
Haegeman, L.: Introduction to Government and Binding Theory. Cambridge: Blackwell (1991)
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) 126–140
McRae, K., Ferretti, T. R., and Amyote, L.: Thematic Roles as Verb-specific Concepts, Language and Cognitive Processes, 12 (2/3) (1997) 137–176
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)
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) 852–857
Rosenblatt, F.: The Perceptron: A Perceiving and Recognizing Automaton, Report 85-460-1, Project PARA, Cornell Aeronautical Laboratory, Ithaca, New York (1957)
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)
Setiono, R. and Liu, H.: Symbolic Representation of Neural Networks, IEEE Computer, Vol. 29, No. 3 (1996) 71–77
Towell, G. G. and Shavlik, J. W.: Extracting Refined Rules from Knowledge-based Neural Networks, Machine Learning, 13 (1993) 71–101
Waltz, D. L. and Pollack, J. B.: Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretations. Cognitive Science 9 (1985) 51–74
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcia Rosa, J.L., Françozo, E. (2000). Linguistic Relations Encoding in a Symbolic- Connectionist Hybrid Natural Language Processor. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_27
Download citation
DOI: https://doi.org/10.1007/3-540-44399-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41276-2
Online ISBN: 978-3-540-44399-5
eBook Packages: Springer Book Archive