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Statistics and Computing

, Volume 2, Issue 2, pp 105–114 | Cite as

Probabilistic text understanding

  • Robert P. Goldman
  • Eugene Charniak
Papers

Abstract

We discuss a new framework for text understanding. Three major design decisions characterize this approach. First, we take the problem of text understanding to be a particular case of the general problem of abductive inference. Second, we use probability theory to handle the uncertainty which arises in this abductive inference process. Finally, all aspects of natural language processing are treated in the same framework, allowing us to integrate syntactic, semantic and pragmatic constraints. In order to apply probability theory to this problem, we have developed a probabilistic model of text understanding. To make it practical to use this model, we have devised a way of incrementally constructing and evaluating belief networks. We have written a program,wimp3, to experiment with this framework. To evaluate this program, we have developed a simple ‘single-blind’ testing method.

Keywords

Natural language processing Bayesian belief networks Bayesian networks 

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Copyright information

© Chapman & Hall 1992

Authors and Affiliations

  • Robert P. Goldman
    • 1
  • Eugene Charniak
    • 2
  1. 1.Department of Computer ScienceTulane UniversityNew OrleansUSA
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA

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