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Probabilistic text understanding

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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.

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References

  • Carroll, G. and Charniak, E. (1989) Finding plans with a marker-passer, inProceedings of the Plan-recognition Workshop.

  • Charniak, E. (1986) A neat theory of marker passing, inProceedings of the Fifth National Conference on Artificial Intelligence, pp. 584–589.

  • Charniak, E. and Goldman, R. P. (1988) A logic for semantic interpretation, inProceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 87–94.

  • Charniak, E. and Goldman, R. P. (1989) A semantics for probabilistic quantifier-free first-order languages, with particular application to story understanding, inProceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 1074–1079.

  • Charniak, E. and Goldman, R. P. (1990) Plan recognition in stories and in life, inUncertainty in Artificial Intelligence 5, Henrion, M., Schachter, R. D., Kanal, L. N. and Lemmer, J. F. (eds). North-Holland, Amsterdam.

    Google Scholar 

  • Charniak, E. and Goldman, R. P. (1991) Probabilistic abduction for plan-recognition. Technical Report CS-91-12, Brown University Computer Science Department, Providence, RI.

    Google Scholar 

  • Charniak, E. and McDermott, D. (1985)Introduction to Artificial Intelligence, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Chin, H. L. and Cooper, G. F. (1987) Stochastic simulation of Bayesian belief networks, inProceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, pp. 106–113.

  • Cooper, G. F. (1987) Probabilistic inference using belief networks is NP-hard. Technical Report KSL-87-27, Medical Computer Science Group, Stanford University.

  • Cox, R. T. (1946) Probability frequency and reasonable expectation.American Journal of Physics,14, 1–13.

    Google Scholar 

  • Cullingford, R. E. (1978) Script application: computer understanding of newspaper stories. Technical Report, Department of Computer Science, Yale University, New Haven, CT.

    Google Scholar 

  • D'Ambrosio, B. (1988a) A hybrid approach to reasoning under uncertainty.International Journal of Approximate Reasoning 2, 29–45.

    Google Scholar 

  • D'Ambrosio, B. (1988b) Process, structure and modularity in reasoning with uncertainty, inThe Fourth Workshop on Uncertainty in Artificial Intelligence, pp. 64–72.

  • deKleer, J. (1986) An assumption-based TMS.Artificial Intelligence,28, 127–162.

    Google Scholar 

  • Goldman, R. P. (1990) A probabilistic approach to language understanding. Technical Report CS-90-34, Computer Science Department, Brown University.

  • Goldman, R. P. and Charniak, E. (1990) Dynamic construction of belief networks, inProceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pp. 90–97.

  • Grosz, B. J. and Sidner, C. (1986) Attention, intention and the structure of discourse.Computational Linguistics,12.

  • Hobbs, J. R. and Martin, P. (1987) Local pragmatics, inProceedings of the 10th International Joint Conference on Artificial Intelligence, pp. 520–523.

  • Hobbs, J. R., Stickel, M., Martin, P. and Edwards, D. (1988) Interpretation as abduction, inProceedings of the 26th Annual Meeting of the ACL, pp. 95–103.

  • Jensen, F. V. (1990) Bayesian updating in recursive graphical models by local computations. Technical Report R 89-15, Institute for Electornic Systems, Department of Mathematics and Computer Science, University of Aalborg, Aalborg, Denmark.

    Google Scholar 

  • Kjærulff, U. (1990) Triangulations of graphs—algorithms giving small total clique size. Technical Report R 90-09, Institute for Electronic Systems, Department of Mathematics and Computer Science, University of Aalborg, Aalborg, Denmark.

    Google Scholar 

  • Laskey, K. B. and Lehner, P. E. (1988) Belief maintenance: an integrated approach to uncertainty management, inProceedings of the Seventh National Conference on Artificial Intelligence, pp. 210–214.

  • Lauritzen, S. L. and Spiegelhalter, D. J. (1988) Local computations with probabilities on graphical structures and their application to expert systems.Journal of the Royal Statistical Society,50, 157–224.

    Google Scholar 

  • Nilsson, N. (1986) Probabilistic logic.Artificial Intelligence,28, 71–88.

    Google Scholar 

  • Norvig, P. (1987) Inference in text understanding, inProceedings of the Sixth National Conference on Artificial Intelligence, pp. 561–565.

  • Palmer, M. and Finin, T. (1990) Workshop on the evaluation of natural language processing systems.Computational Linguistics 16, 175–181.

    Google Scholar 

  • Pearl, J. (1988)Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, Los Altos, CA.

    Google Scholar 

  • Shimony, S. and Charniak, E. (1990) A new algorithm for finding map assignments to belief networks, inProceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pp. 98–103.

  • Srinivas, S. and Breese, J. (1989)IDEAL: Influence Diagram Evaluation and Analysis in Lisp, Rockwell International Science Center, Palo Alto, CA.

    Google Scholar 

  • Webber, B. L. (1987) The interpretation of tense in discourse, inProceedings of the 25th Annual Meeting of the ACL, pp. 147–154.

  • Wilensky, R. (1983)Planning and Understanding, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Wong, D. (1981) Language comprehension in a problem solver, inProceedings of the 4th International Joint Conference on Artificial Intelligence, pp. 7–12.

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Authors and Affiliations

  1. Department of Computer Science, Tulane University, 301 Stanley Thomas Hall, 70118, New Orleans, LA, USA

    Robert P. Goldman

  2. Department of Computer Science, Brown University, Box 1910, 02912, Providence, RI, USA

    Eugene Charniak

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  1. Robert P. Goldman
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  2. Eugene Charniak
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Goldman, R.P., Charniak, E. Probabilistic text understanding. Stat Comput 2, 105–114 (1992). https://doi.org/10.1007/BF01889589

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