Bayesian Inference Semantics: A Modelling System and A Test Suite
dc.contributor.author | Bernardy, J-P | en_US |
dc.contributor.author | Blanck, R | en_US |
dc.contributor.author | Chatzikyriakidis, S | en_US |
dc.contributor.author | Lappin, S | en_US |
dc.contributor.author | Maskharashvili, A | en_US |
dc.date.accessioned | 2020-08-24T09:27:02Z | |
dc.date.issued | 2019-06-06 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/66547 | |
dc.description.abstract | We present BIS, a Bayesian Inference Seman- tics, for probabilistic reasoning in natural lan- guage. The current system is based on the framework of Bernardy et al. (2018), but de- parts from it in important respects. BIS makes use of Bayesian learning for inferring a hy- pothesis from premises. This involves estimat- ing the probability of the hypothesis, given the data supplied by the premises of an argument. It uses a syntactic parser to generate typed syn- tactic structures that serve as input to a model generation system. Sentences are interpreted compositionally to probabilistic programs, and the corresponding truth values are estimated using sampling methods. BIS successfully deals with various probabilistic semantic phe- nomena, including frequency adverbs, gener- alised quantifiers, generics, and vague predi- cates. It performs well on a number of interest- ing probabilistic reasoning tasks. It also sus- tains most classically valid inferences (instan- tiation, de Morgan’s laws, etc.). To test BIS we have built an experimental test suite with examples of a range of probabilistic and clas- sical inference patterns. | en_US |
dc.format.extent | 263 - 272 (9) | en_US |
dc.language | English | en_US |
dc.publisher | Association of Computational Linguistics | en_US |
dc.relation.ispartof | Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM) 2019 | en_US |
dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | Bayesian inference, probabilistic semantics, computational linguistics | en_US |
dc.title | Bayesian Inference Semantics: A Modelling System and A Test Suite | en_US |
dc.type | Other | |
dc.rights.holder | © 2019 The Author(s) | |
pubs.confidential | false | en_US |
pubs.issue | 2019 | en_US |
pubs.notes | Not known | en_US |
pubs.place-of-publication | Minneapolis MN | en_US |
pubs.publisher-url | https://www.aclweb.org/anthology/S19-1029/ | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | International Recruitment Grant::Swedish Research Council | en_US |
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Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.