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Flexible Parsing Architectures for NLP Applications

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 2175)

Abstract

The requirements of different NLP applications have strong implications on the design and implementation of the related syntactic recognisers. In this paper, a fine-grained modular parser design framework is presented. Our aim is to reduce the design of a parsing processors to the composition of a pool of basic modules. Results over sample parsers and criteria for optimising coverage and accuracy are discussed.

Keywords

  • Noun Phrase
  • Lexical Knowledge
  • Annotate Corpus
  • Translation Algorithm
  • Parsing System

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2001 Springer-Verlag Berlin Heidelberg

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Basili, R., Pazienza, M.T., Zanzotto, F.M. (2001). Flexible Parsing Architectures for NLP Applications. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_31

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  • DOI: https://doi.org/10.1007/3-540-45411-X_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42601-1

  • Online ISBN: 978-3-540-45411-3

  • eBook Packages: Springer Book Archive