1 research outputs found
Learning Type-Driven Tensor-Based Meaning Representations
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.Comment: Submitted as part of the open review process for ICLR'14. The paper
contains 10 pages, 3 figures, 4 table