Abstract
This paper describes a hybrid model which integrates symbolic and connectionist techniques for the analysis of noun phrases. Our model consists of three levels: (1) a distributed connectionist level, (2) a localist connectionist level, and (3) a symbolic level. While most current systems in natural language processing use techniques from only one of these three levels, our model takes advantage of the virtues of all three processing paradigms. The distributed connectionist level provides a learned semantic memory model. The localist connectionist level integrates semantic and syntactic constraints. The symbolic level is responsible for restricted syntactic analysis and concept extraction. We conclude that a hybrid model is potentially stronger than models that rely on only one processing paradigm.
We would like to thank Claire Cardie, David Lewis, Ellen Riloff, and the anonymous reviewers for comments on earlier drafts of this paper. This research was supported by the Advanced Research Projects Agency of the Department of Defense, monitored by the Office of Naval Research under contract No. N00014-87-K-0238, the Office of Naval Research, under a University Research Initiative Grant, Contract No. N00014-86-K-0764 and NSF Presidential Young Investigators Award NSFIST-8351863.
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Wermter, S., Lehnert, W.G. (1992). A Hybrid Symbolic/Connectionist Model for Noun Phrase Understanding. In: Sharkey, N. (eds) Connectionist Natural Language Processing. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2624-3_6
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DOI: https://doi.org/10.1007/978-94-011-2624-3_6
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