Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Representation, Coherence and Inference

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Approaches to story comprehension within several fields (computational linguistics, cognitive psychology, and artificial intelligence) are compared. Central to this comparison is an overview of much recent research in cognitive psychology, which is often not incorporated into simulations of comprehension (particularly in artificial intelligence). The theoretical core of this experimental work is the establishment of coherence via inference-making.

The definitions of coherence and inference-making in this paper incorporate some of this work in cognitive psychology. Three major research methodologies are examined in the light of these definitions: scripts, spreading activation, and abduction.

This analysis highlights several deficiencies in current models of comprehension. One deficiency of concern is the `one-track' behaviour of current systems, which pursue a monostratal representation of each story. In contrast, this paper emphasises a view of adaptive comprehension which produces a `variable-depth' representation. A representation is pursued to the extent specified by the comprehender's goals; these goals determine the amount of coherence sought by the system, and hence the `depth' of its representation. Coherence is generated incrementally via inferences which explain the co-occurrence of story elements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Alterman, R. (1985). A Dictionary Based on Concept Coherence. Artificial Intelligence 25(2): 153-186.

    Google Scholar 

  • Alterman, R. (1991). Understanding and Summarisation. Artificial Intelligence Review 5(4); 239-254.

    Google Scholar 

  • Alterman, R. & Bookman, L.A. (1990). Some Computational Experiments in Summarisation. Discourse Processes 13(2): 143-174.

    Google Scholar 

  • Alterman, R. & Bookman, L.A. (1992). Reasoning About a Semantic Memory Encoding of the Connectivity of Events. Cognitive Science 16(2): 205-232.

    Google Scholar 

  • Anderson, S. & Slator, B.M. (1990). Requiem for a Theory: the 'story Grammar' Story. Journal of Experimental and Theoretical Artificial intelligence 2(3): 253-275.

    Google Scholar 

  • Anderson, J. R. (1983). The Architecture of Cognition. Harvard University Press: Cambridge, MA.

    Google Scholar 

  • Asher, N. (1993). Reference to Abstract Objects in Discourse. Kluwer Academic: Dordrecht.

    Google Scholar 

  • Black, J. B. & Wilensky, R. (1979). An Evaluation of Story Grammars. Cognitive Science 3(3): 213-230.

    Google Scholar 

  • Black, J. B. & Bower, G. H. (1980). Story Understanding as Problem Solving. Poetics 9(1-3): 223-250.

    Google Scholar 

  • Charniak, E. (1986). A Neat Theory of Marker Passing. Proceedings of the 5th National Conference on Artificial Intelligence (AAAI-86), 584-588. Morgan Kaufmann: Philadelphia.

    Google Scholar 

  • Charniak, E. & Goldman, R. (1989). A Semantics for Probabilistic Quantifier-Free First-Order Languages, with Particular Application to Story Understanding. Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI-89), 1074-1079. Morgan Kaufmann: Detroit.

    Google Scholar 

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

    Google Scholar 

  • Correira, A. (1980). Computing Story Trees. American Journal of Computational Linguistics 6(3-4): 135-149.

    Google Scholar 

  • Crystal, D. (1971). Linguistics. Harmondsworth: Penguin.

    Google Scholar 

  • Dahlgren, K. (1988). Naï ve Semantics for Natural Language Understanding. Kluwer Academic: Norwell, MA.

    Google Scholar 

  • Dahlgren, K., McDowell, J. & Stabler, E. P. (1989). Knowledge Representation for Commonsense Reasoning with Text. Computational Linguistics 15(3): 149-170.

    Google Scholar 

  • DeJong, G. F. (1979). Prediction and Substantiation: A New Approach to Natural Language Processing. Cognitive Science 3(3): 251-273.

    Google Scholar 

  • Dyer, M. G. (1992). Scripts. In Shapiro, S. C. (ed.) Encyclopaedia of Artificial Intelligence, 1443-1460. John Wiley and Sons: New York.

    Google Scholar 

  • Eberle, K. (1992). On Representing the Temporal Structure of a Natural Language Text. Proceedings of the 15th International Conference on Computational Linguistics (COLING-92), 288-294. Association for Computational Linguistics: Nantes.

    Google Scholar 

  • Frisch, A. M. & Perlis, D. (1981). A Re-Evaluation of Story Grammars. Cognitive Science 5(1): 76-86.

    Google Scholar 

  • Garnham, A. (1983). What's Wrong With Story Grammars. Cognition 15: 145-154.

    Google Scholar 

  • Garnham, A. (1987). Mental Models as Representations of Discourse and Text. Ellis-Horwood: Chichester.

    Google Scholar 

  • Graesser, A. C., Singer, M. & Trabasso, T. (1994). Constructing Inferences During Narrative Text Comprehension. Psychological Review 101(3): 371-395.

    Google Scholar 

  • Hobbs, J. R. (1979). Coherence and Coreference. Cognitive Science 3(1): 67-90.

    Google Scholar 

  • Hobbs, J. R. & Kameyama, M. (1990). Translation by Abduction. Proceedings of the 13th International Conference on Computational Linguistics (COLING-90), 155-161. Helsinki University: Helsinki.

    Google Scholar 

  • Hobbs, J. R., Stickel, M. E., Appelt, D. E. & Martin, P. (1993). Interpretation as Abduction. Artificial Intelligence 63(1-2): 69-142.

    Google Scholar 

  • Keefe, D. E. & McDaniel, M. A. (1993). The Time Course and Durability of Predictive Inferences. Journal of Memory and Language 32(4): 446-463.

    Google Scholar 

  • Kintsch, W. (1988). The Role of Knowledge in Discourse Comprehension: A Construction-Integration Model. Psychological Review 95(2): 163-182.

    Google Scholar 

  • Kintsch, W. & van Dijk, T. A. (1978). Toward a Model of Text Comprehension and Production. Psychological Review 85(5): 363-394.

    Google Scholar 

  • Leake, D. B. (1994). Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation. Technical Report p-95-07, Computer Science Department, Indiana University, Indianapolis.

    Google Scholar 

  • Lehnert, W. G. (1982). Plot Units: A Narrative Summarisation Strategy. In Lehnert, W. G. and Ringle, M. H. (eds.) Strategies for Natural Language Processing, 375-414. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • Lehnert, W. G., Dyer, M. G., Johnson, P. N., Yang, C. J. & Harley, S. (1983). BORIS-an Experiment in In-Depth Understanding of Narratives. Artificial Intelligence 20(1): 15-62.

    Google Scholar 

  • Mandler, J. M. (1984). Stories, Scripts, and Scenes: Aspects of Schema Theory. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • Mandler, J. M. & Johnson, N. S. (1977). Remembrance of Things Parsed: Story Structure and Recall. Cognitive Psychology 9(1): 111-151.

    Google Scholar 

  • McKoon, G. & Ratcliff, R. (1992). Inference during Reading. Psychological Review 99(3): 440-466.

    Google Scholar 

  • Minsky, M. L. (1975). A Framework for Representing Knowledge. In Winston, P. H. (ed.) The Psychology of Computer Vision, 211-277. McGraw-Hill: New York.

    Google Scholar 

  • Morris, P. (1978). Models of Long-Term Memory. In Gruneberg, M. M. & Morris, P. (eds.) Aspects of Memory, 84-103. Methuen: London.

    Google Scholar 

  • Murray, J. D., Klin, C.M. & Myers, J. L. (1993). Forward Inferences in Narrative Text. Journal of Memory and Language 32(4): 464-473.

    Google Scholar 

  • Myers, J. L., O'Brien, E. J., Albrecht, J. E. & Mason, R. A. (1994). Maintaining Global Coherence during Reading. Journal of Experimental Psychology: Learning, Memory, and Cognition 20(4): 876-886.

    Google Scholar 

  • Ng, H. T. & Mooney, R. J. (1990). On the Role of Coherence in Abductive Explanation. Proceedings of the 8th National Conference on Artificial Intelligence (AAAI-90), 337-342. MIT Press: Boston, MA.

    Google Scholar 

  • Noordman, L. G. M. & Vonk, W. (1992). Reader's Knowledge and the Control of Inferences in Reading. Language and Cognitive Processes 7(3-4): 373-391.

    Google Scholar 

  • Norvig, P. (1989). Marker Passing as a Weak Method for Text Inferencing. Cognitive Science 13(4): 569-620.

    Google Scholar 

  • Norvig, P. & Wilensky, R. (1990). A Critical Evaluation of Commensurable Abduction Models for Semantic Interpretation. Proceedings of the 13th International Conference of Computational Linguistic (COLING-90), 224-230. Helsinki University: Helsinki.

    Google Scholar 

  • Rumelhart, D. E. (1975). Notes on a Schema for Stories. In Bobrow, D. G. and Collins, A. (eds.) Representation and Understanding, 211-236. Academic Press: New York.

    Google Scholar 

  • Schank, R. & Wilensky, R. (1978). A Goal-Directed Production System for Story Understanding. In Waterman, D. A. and Hayes-Roth, F. (eds.) Pattern-Directed Inference Systems, 415-430. Academic Press: Orlando, FL.

    Google Scholar 

  • Schank, R. & Abelson, R. P. (1977). Scripts, Plans, Goals and Understanding. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • Singer, M., Graesser, A. C. & Trabasso, T. (1994). Minimal or Global Inference during Reading. Journal of Memory and Language 33(4): 421-441.

    Google Scholar 

  • Smith, E. (1997). A Computational Model of On-Line Story Understanding. Ph.D. progress report 97-2, School of Computer Science, University of Birmingham.

  • Stickel, M. (1990). Rationale and Methods for Adbuctive Reasoning in Natural-Language Interpretation. Proceedings of Natural Language and Logic: International Scientific Symposium, 233-252. Springer Verlag: Hamburg.

    Google Scholar 

  • Thagard, P. (1989). Explanatory Coherence. Behavioral and Brain Science 12(3): 435-502.

    Google Scholar 

  • Thorndyke, P. (1977). Cognitive Structures in Comprehension and Memory of Narrative Discourse. Cognitive Psychology 9(1): 77-110.

    Google Scholar 

  • Trabasso, T. & Magliano, J. P. (1996). Conscious Understanding during Comprehension. Discourse Processes 21(3): 255-287.

    Google Scholar 

  • Trabasso, T. & van den Broek, P. (1985). Causal Thinking and the Representation of Narrative Events. Journal of Memory and Language 24(5): 612-630.

    Google Scholar 

  • Trabasso, T., van den Broek, P. & Suh, S.Y. (1989). Logical Necessity and Transitivity of Causal Relations in Stories. Discourse Processes 12(1): 1-25.

    Google Scholar 

  • van den Broek, P. (1990a). The Causal Inference Maker: Towards a ProcessModel of Inference Generation in Text Comprehension. In Balota, D. A., Flores d'Arcais, G. B. and Rayner, K. (eds.) Comprehension Processes in Reading, 423-445. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • van den Broek, P. (1990b). Causal Inferences and the Comprehension of Narrative Texts. The Psychology of Learning and Motivation 25: 175-196.

  • van den Broek, P. (1994). Comprehension and Memory of Narrative Texts: Inferences and Coherence. In Gernsbacher, M. A. (ed.) Handbook of Psycholinguistics, 539-588. Academic Press: London.

    Google Scholar 

  • van den Broek, P., Risden, K. & Husebye-Hartmann, E. (1995). The Role of Readers' Standards for Coherence in the Generation of Inferences during Reading. In Lorch, R. F. and O'Brien, E. J. (eds.) Sources of Coherence in Reading, 353-373. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • van den Broek, P. & Trabasso, T. (1986). Causal Networks versus Goal Hierarchies in Summarising Text. Discourse Processes 9(1): 1-15.

    Google Scholar 

  • van Dijk, T. A. (1977). Semantic Macro-Structures and Knowledge Frames in Discourse Comprehension. In Just, M. A. and Carpenter, P. A. (eds.) Cognitive Processes in Comprehension, 3-32. Lawrence Erlbaum: Hillsdale, NJ.

    Google Scholar 

  • Whitney, P., Ritchie, B. G. & Clark, M. B. (1991). Working-Memory Capacity and the Use of Elaborative Inferences in Text Comprehension. Discourse Processes 14(2): 133-145.

    Google Scholar 

  • Wilensky, R. (1983). Story Grammar versus Story Points. Behavioral and Brain Sciences 6(4): 579-623.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Edgbaston, B15 2TT, UK

    Elliot Smith & Peter Hancox

Authors
  1. Elliot Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Hancox
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Smith, E., Hancox, P. Representation, Coherence and Inference. Artificial Intelligence Review 15, 295–323 (2001). https://doi.org/10.1023/A:1011092219561

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011092219561

Search

Navigation