Functional Distributional Semantics
Published version
Peer-reviewed
Repository URI
Repository DOI
Loading...
Type
Conference Object
Change log
Authors
Emerson, Guy https://orcid.org/0000-0002-3136-9682
Copestake, Ann https://orcid.org/0000-0003-0347-946X
Abstract
Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.
Description
Keywords
cs.CL, cs.CL
Journal Title
Proceedings of the 1st Workshop on Representation Learning for NLP
Conference Name
1st Workshop on Representation Learning for NLP
Journal ISSN
Volume Title
Publisher
The Association for Computational Linguistics