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Bayesian Inference Semantics: A Modelling System and A Test Suite

Paper i proceeding
Författare Jean-Philippe Bernardy
Rasmus Blanck
Stergios Chatzikyriakidis
Shalom Lappin
Aleksandre Maskharashvili
Publicerad i Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM), 6-7 June 2019, Minneapolis, Minnesota, USA / Rada Mihalcea, Ekaterina Shutova, Lun-Wei Ku, Kilian Evang, Soujanya Poria (Editors)
ISBN 9781510887596
Förlag Association for Computational Linguistics
Publiceringsår 2019
Publicerad vid Institutionen för filosofi, lingvistik och vetenskapsteori
Språk en
Länkar https://www.aclweb.org/anthology/S1...
Ämnesord Bayesian models, probabilistic semantics, probabilistic programming languages, Markov Chain Monte Carlo sampling, generalised quantifiers, vague predicates, compositionality, inference
Ämneskategorier Datorlingvistik, Lingvistik

Sammanfattning

We present BIS, a Bayesian Inference Semantics, for probabilistic reasoning in natural language. The current system is based on the framework of Bernardy et al. (2018), but departs from it in important respects. BIS makes use of Bayesian learning for inferring a hypothesis from premises. This involves estimating the probability of the hypothesis, given the data supplied by the premises of an argument. It uses a syntactic parser to generate typed syntactic 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 phenomena, including frequency adverbs, generalised quantifiers, generics, and vague predicates. It performs well on a number of interesting probabilistic reasoning tasks. It also sustains most classically valid inferences (instantiation, de Morgan’s laws, etc.). To test BIS we have built an experimental test suite with examples of a range of probabilistic and classical inference patterns.

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