Bayesian Inference Semantics for Natural Language

• We present a Bayesian Inference Semantics for natural language, which computes the probability conditions of sentences compositionally, through semantic functions matching with the types of their syntactic constituents. This system captures the vagueness of classifier terms and scalar modifiers. It also offers a straightforward treatment of the sorites paradox. Our framework expresses probabilistic inferences, which rely on lexically encoded priors, and it captures the effect of informational update on these inferences, through Bayesian modelling. The central device with which we represent probabilistic interpretation is the assignment of measurable spaces to objects and properties. We estimate the probability of a predication by measuring the density of relevant objects in the space of the property that the predicate denotes. We explore two alternative models for the priors. The first one is based on Gaussian distributions, but it exhibits computational intractability with some cases of Monte Carlo sampling. The second is based on uniform densities, and in a number of important instances, it allows us to avoid Monte Carlo sampling. We construct a test suite to illustrate the range of syntactic and semantic constructions, and the inference types, that our system covers.

Ämnesord

NATURVETENSKAP  -- Matematik -- Algebra och logik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Algebra and Logic (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

HUMANIORA  -- Språk och litteratur -- Jämförande språkvetenskap och allmän lingvistik (hsv//swe)

HUMANITIES  -- Languages and Literature -- General Language Studies and Linguistics (hsv//eng)

Nyckelord

Probabilistic semantics

Bayesian inference

compositionality

measurable spaces

vagueness

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