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Predicates as Boxes in Bayesian Semantics for Natural Language

Conference paper
Authors Jean-Philippe Bernardy
Rasmus Blanck
Stergios Chatzikyriakidis
Shalom Lappin
Aleksandre Maskharashvili
Published in Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa 2019), 30 September-2 October, 2019, Turku, Finland / Mareike Hartmann, Barbara Plank (Editors)
ISBN 978-91-7929-995-8
ISSN 1650-3686
Publisher Linköping University Electronic Press
Place of publication Linköping
Publication year 2019
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links https://www.aclweb.org/anthology/W1...
Keywords Bayesian models, probabilistic semantics, generalised quantifiers, vague predicates, compositionality, inference
Subject categories Computational linguistics, Linguistics

Abstract

In this paper, we present a Bayesian approach to natural language semantics. Our main focus is on the inference task in an environment where judgments require probabilistic reasoning. We treat nouns, verbs, adjectives, etc. as unary predicates, and we model them as boxes in a bounded domain. We apply Bayesian learning to satisfy constraints expressed as premises. In this way we construct a model, by specifying boxes for the predicates. The probability of the hypothesis (the conclusion) is evaluated against the model that incorporates the premises as constraints.

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