Efficiency in ambiguity: two models of probabilistic semantics for natural language

Clarke, Daoud and Keller, Bill (2015) Efficiency in ambiguity: two models of probabilistic semantics for natural language. IWCS 2015: 11th International Conference on Computational Semantics, Queen Mary University, London, April 15 - 17, 2015. Published in: Proceedings of the 11th International Conference on Computational Semantics. 129-139. Association for Computational Linguistics, London, UK. ISBN 978-1-941643-33-4

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This paper explores theoretical issues in constructing an adequate probabilistic semantics for natural language. Two approaches are contrasted. The first extends Montague Semantics with a probability distribution over models. It has nice theoretical properties, but does not account for the ubiquitous nature of ambiguity; moreover inference is NP hard. An alternative approach is described in which a sequence of pairs of sentences and truth values is generated randomly. By sacrificing some of the nice theoretical properties of the first approach it is possible to model ambiguity naturally; moreover inference now has polynomial time complexity. Both approaches provide a compositional semantics and account for the gradience of semantic judgements of belief and inference.

Item Type: Conference Proceedings
Keywords: Computational Semantics, Probabilistic Semantics, Model Theory, Montague Grammar
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: P Language and Literature > P Philology. Linguistics > P0098 Computational linguistics. Natural language processing
Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
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Depositing User: Bill Keller
Date Deposited: 22 Apr 2015 10:24
Last Modified: 11 Feb 2019 17:15
URI: http://sro.sussex.ac.uk/id/eprint/53714

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A Unified Model of Compositional and Distributional Semantics: Theory and ApplicationsUnsetEPSRCEP/I037458/1