Hopfield Models as Nondeterministic Finite-state Machines

M.F.J. Drossaers

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    The use of neural networks for integrated linguistic analysis may be profitable. This paper presents the first results of our research on that subject: a Hopfield model for syntactical analysis. We construct a neural network as an implementation of a bounded push-down automaton, which can accept context-free languages with limited center-embedding. The network's behavior can be predicted a priori, so the presented theory can be tested. The operation of the network as an implementation of the acceptor is provably correct. Furthermore we found a solution to the problem of spurious states in Hopfield models: we use them as dynamically constructed representations of sets of states of the implemented acceptor. The so-called neural-network acceptor we propose, is fast but large.
    Original languageEnglish
    Title of host publicationCOLING-92
    Subtitle of host publicationProceedings of the fifteenth International Conference on Computational Linguistics
    EditorsCh. Boitet
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages7
    Publication statusPublished - 23 Jul 1992
    Event14th International Conference on Computational Linguistics, COLING 1992 - Nantes, France
    Duration: 23 Aug 199228 Aug 1992
    Conference number: 14


    Conference14th International Conference on Computational Linguistics, COLING 1992
    Abbreviated titleCOLING


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