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Fluency in Natural Language Reports

  • Chapter
Natural Language Generation Systems

Part of the book series: Symbolic Computation ((1064))

Abstract

Pinpointing the factors that are responsible for fluency in written text is a traditionally difficult problem. This paper argues that the source of that difficulty is the knowledge-intensive nature of language generation. Not only are a variety of types of knowledge required, such as semantic, lexical, syntactic, grammar and discourse knowledge, but those various forms of knowledge are used in a highly integrated fashion during the generation of text. In this paper, the technique of knowledge-based report generation is described, and samples of text produced by a knowledge-based report generator are presented. A variety of fluency skills and defects found in the samples are identified and categorized. It is argued that the fluency skills exhibited are the result of the effective identification and integration of some specific knowledge processes, and that the fluency defects exhibited are the result of incomplete or poorly integrated knowledge processes.

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Authors
  1. Karen Kukich
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Editor information

Editors and Affiliations

  1. Brattle Research Corporation, Cambridge, MA, 02138, USA

    David D. McDonald

  2. Institute of Informatics, Warsaw, Warsaw University, PKin, pok. 850, PL.00.901, Warszawa, Poland

    Leonard Bolc

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© 1988 Springer-Verlag New York Inc.

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Kukich, K. (1988). Fluency in Natural Language Reports. In: McDonald, D.D., Bolc, L. (eds) Natural Language Generation Systems. Symbolic Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3846-1_8

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  • DOI: https://doi.org/10.1007/978-1-4612-3846-1_8

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-8374-4

  • Online ISBN: 978-1-4612-3846-1

  • eBook Packages: Springer Book Archive

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