Understanding Natural Language
Understanding Natural Language
A Study of Qualitative and Geometric Knowledge in Reasoning about Motion
A Study of Qualitative and Geometric Knowledge in Reasoning about Motion
Pattern-matching rules for the recognition of natural language dialogue expressions.
Pattern-matching rules for the recognition of natural language dialogue expressions.
Computer Understanding of Physics Problems Stated in Natural Language.(Dissertation), also Technical Report NL-30
TINLAP '75 Proceedings of the 1975 workshop on Theoretical issues in natural language processing
TINLAP '75 Proceedings of the 1975 workshop on Theoretical issues in natural language processing
The relation of grammar to cognition: a synopsis
TINLAP '78 Proceedings of the 1978 workshop on Theoretical issues in natural language processing
On the spatial uses of prepositions
ACL '80 Proceedings of the 18th annual meeting on Association for Computational Linguistics
Understanding scene descriptions as event simulations
ACL '80 Proceedings of the 18th annual meeting on Association for Computational Linguistics
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This paper explores the problem of judging whether or not an English sentence could correspond to a real world situation or event which is literally, physically plausible, and the related problem of representing the different possible physical situations. The judgement of plausibility can be made at a high level by checking semantic marker restrictions on verb case frame constituents. Often, however, plausibility judgement can only be based on the results of an attempt to construct (imagine) a scene that corresponds to the sentence, and which does not violate "common sense" (i.e. relevant physical laws and expected, stereotyped behavior). Methods are presented for constructing representations for different scenes which could correspond to a sentence. These methods incorporate (1) "subscripts" (sequences of scenes which comprise an event, with attached preconditions and postconditions) to express different verb senses, (2) object representations which express properties such as shape, size, weight, strength, and behavior under common conditions; (5) physical laws, encoded as constraints on behavior; (4) representation of context; and (5) robot problem solving-like methods to fit all this material together.