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

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conference contribution
posted on 2023-06-08, 20:34 authored by Daoud Clarke, Bill Keller
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.

Funding

A Unified Model of Compositional and Distributional Semantics: Theory and Applications; EPSRC; EP/I037458/1

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Proceedings of the 11th International Conference on Computational Semantics

Publisher

Association for Computational Linguistics

Page range

129-139

Event name

IWCS 2015: 11th International Conference on Computational Semantics

Event location

Queen Mary University, London

Event type

conference

Event date

April 15 - 17, 2015

Place of publication

London, UK

ISBN

978-1-941643-33-4

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • No

Legacy Posted Date

2015-04-22

First Open Access (FOA) Date

2019-02-11

First Compliant Deposit (FCD) Date

2015-04-21

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