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Multimodal Semantic Simulations of Linguistically Underspecified Motion Events ...
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Generating Simulations of Motion Events from Verbal Descriptions ...
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Abstract:
In this paper, we describe a computational model for motion events in natural language that maps from linguistic expressions, through a dynamic event interpretation, into three-dimensional temporal simulations in a model. Starting with the model from (Pustejovsky and Moszkowicz, 2011), we analyze motion events using temporally-traced Labelled Transition Systems. We model the distinction between path- and manner-motion in an operational semantics, and further distinguish different types of manner-of-motion verbs in terms of the mereo-topological relations that hold throughout the process of movement. From these representations, we generate minimal models, which are realized as three-dimensional simulations in software developed with the game engine, Unity. The generated simulations act as a conceptual "debugger" for the semantics of different motion verbs: that is, by testing for consistency and informativeness in the model, simulations expose the presuppositions associated with linguistic expressions and ... : 11 pages, 5 figures, *SEM workshop, COLING 2014 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1610.01713 https://arxiv.org/abs/1610.01713
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Workset Creation for Scholarly Analysis and Data Capsules (WCSA+DC): Laying the foundations for secure computation with copyrighted data in the HathiTrust Research Center, Phase I
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33 |
Temporal Annotation in the Clinical Domain.
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In: Transactions of the Association for Computational Linguistics, vol 2 (2014)
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Computational Models of Language Meaning in Context (Dagstuhl Seminar 13462)
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Computational Models of Language Meaning in Context (Dagstuhl Seminar 13462) ...
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C3: The Compositional Construction of Context. A New, More Effective and Efficient Way to Marshal Inferences from Background Knowledge
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In: DTIC (2014)
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Natural language annotation for machine learning : [a guide to corpus-building for applications]
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IDS Mannheim
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