Repository logo

Semantic Composition via Probabilistic Model Theory

Accepted version


Conference Object

Change log


Semantic composition remains an open problem for vector space models of semantics. In this paper, we explain how the probabilistic graphical model used in the framework of Functional Distributional Semantics can be interpreted as a probabilistic version of model theory. Building on this, we explain how various semantic phenomena can be recast in terms of conditional probabilities in the graphical model. This connection between formal semantics and machine learning is helpful in both directions: it gives us an explicit mechanism for modelling context-dependent meanings (a challenge for formal semantics), and also gives us well-motivated techniques for composing distributed representations (a challenge for distributional semantics). We present results on two datasets that go beyond word similarity, showing how these semantically-motivated techniques improve on the performance of vector models.



cs.CL, cs.CL

Journal Title

IWCS 2017 - 12th International Conference on Computational Semantics

Conference Name

The 12th International Conference on Computational Semantics

Journal ISSN

Volume Title


ACL Anthology
Schiff Foundation