Learning Type-Driven Tensor-Based Meaning Representations
Authors: Tamara Polajnar, Luana Fagarasan, Stephen Clark
ICLR 2014

Abstract: This paper investigates the learning of 3rd-order tensors representing the semantics of transitive verbs. The meaning representations are part of a type-driven tensor-based semantic framework, from the newly emerging field of compositional distributional semantics. Standard techniques from the neural networks literature are used to learn the tensors, which are tested on a selectional preference-style task with a simple 2-dimensional sentence space. Promising results are obtained against a competitive corpus-based baseline. We argue that extending this work beyond transitive verbs, and to higher-dimensional sentence spaces, is an interesting and challenging problem for the machine learning community to consider.


Peer review:
[Review]:
We have uploaded an updated version which addresses some of the points raised by the reviewers.  The new version will be visible after  Wed, 19 Feb 2014 01:00:00 GMT.