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Approaches to natural language discourse processing

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Abstract

One of the most difficult problems within the field of Artificial Intelligence (AI) is that of processing language by computer, or natural-language processing. A major problem in natural-language processing is to build theories and models of how individual utterances cling together into a coherent discourse. The problem is important because, to properly understand natural language, a computer should have some sense of what it means for a discourse to be coherent and rational. Theories, models and implementations of natural-language processing argue for a measure of coherence based on three themes: meaning, structure, and intention. Most approaches stress one theme over all the others. Their future lies in the integration of components of all approaches. A theory of intention analysis solves, in part, the problem of natural-language dialogue processing. A central principle of the theory is that coherence of natural-language dialogue can be modelled by analysing sequences of intention. The theory of intention analysis has been incorporated within a computational model, called Operating System CONsultant (OSCON), implemented in Quintus Prolog, which understands, and answers in English, English questions about computer operating systems. Theories and implementations of discourse processing will not only enable people to communicate better with computers, but also enable computers to better communicate with people.

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Authors and Affiliations

  1. Department of Computer Science, Regent Court, University of Sheffield, 211 Protobello Street, GB-S1 4DP, Sheffield, England, EC

    Paul Mc Kevitt

  2. Department of Computer Science, University of Exeter, GB-EX4 4PT, Exeter, England, EC

    Derek Partridge

  3. Computing Research Laboratory, Dept. 3CRL, New Mexico State University, Box 30001, 88003-0001, Las Cruces, NM, USA

    Yorick Wilks

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  1. Paul Mc Kevitt
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  2. Derek Partridge
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  3. Yorick Wilks
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Kevitt, P.M., Partridge, D. & Wilks, Y. Approaches to natural language discourse processing. Artif Intell Rev 6, 333–364 (1992). https://doi.org/10.1007/BF00123689

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