A dictionary based on concept coherence
Artificial Intelligence
Introduction to artificial intelligence
Introduction to artificial intelligence
Knowledge representation for commonsense reasoning with text
Computational Linguistics
Requiem for a theory: the “story grammar” story
Journal of Experimental & Theoretical Artificial Intelligence
Artificial Intelligence - Special volume on natural language processing
Naive Semantics for Natural Language Understanding
Naive Semantics for Natural Language Understanding
The Architecture of Cognition
Rationale and Methods for Abductice Reasoning in Natural-Language Interpretation
Proceedings of the International Symposium on Natural Language and Logic
Computational Linguistics
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
A critical evaluation of commensurable abduction models for semantic interpretation
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
On representing the temporal structure of a natural language text
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Toward intelligent support of authoring machinima media content: story and visualization
Proceedings of the 2nd international conference on INtelligent TEchnologies for interactive enterTAINment
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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.