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Tepper, JA; Powell, HM; Palmer-Brown, D (2002)
Publisher: Taylor & Francis (Routledge)
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

ACM Ref: TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES
We describe a deterministic shift-reduce parsing model that combines the advantages of connectionism with those of traditional symbolic models for parsing realistic sub-domains of natural language. It is a modular system that learns to annotate natural language texts with syntactic structure. The parser acquires its linguistic knowledge directly from pre-parsed sentence examples extracted from an annotated corpus. The connectionist modules enable the automatic learning of linguistic constraints and provide a distributed representation of linguistic information that exhibits tolerance to grammatical variation. The inputs and outputs of the connectionist modules represent symbolic information which can be easily manipulated and interpreted and provide the basis for organizing the parse. Performance is evaluated using labelled precision and recall. (For a test set of 4128 words, precision and recall of 75% and 69%, respectively, were achieved.) The work presented represents a significant step towards demonstrating that broad coverage parsing of natural language can be achieved with simple hybrid connectionist architectures which approximate shift-reduce parsing behaviours. Crucially, the model is adaptable to the grammatical framework of the training corpus used and so is not predisposed to a particular grammatical formalism.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Allen, J., 1995, Natural Language Understanding. 2nd Edition, (The Benjamin/Cummings Publishing Company, Inc.).
    • Berg, G., 1992, A connectionist parser with recursive sentence structure and lexical disambiguation. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), Cambridge, MA, pp 32-37, (AAAI Press/MIT Press).
    • BNC Consortium, 1995, British National Corpus. (Oxford University Press).
    • Bod, R., 1996, Monte Carlo Parsing. Recent Advances in Parsing Technology, (Kluwer Academic Publishers, Boston) , pp 255-280.
    • 93.4% 49.0% Labelled Precision 81.7%
    • 75.1% Labelled Recall 76.1%
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

    Title Trust
    47
    47%
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