Learning Distributed Representations of
Concepts Using Linear Relational Embedding
Alberto Paccanaro and Geoffrey Hinton
Gatsby Computational Neuroscience Unit
University College London
Alexandra House
17 Queen Square
London WC1N 3AR
GCNU TR 2000-002 [March 2000]
Abstract
In this paper we introduce Linear Relational Embedding as a means of
learning a distributed representation of concepts from data consisting of binary relations
between concepts. The key idea is to represent concepts as vectors, binary relations as
matrices, and the operation of applying a relation to a concept as a matrix-vector
multiplication that produces an approximation to the related concept. A
repesentation for concepts and relations is learned by maximizing an appropriate
discriminative goodness function using gradient ascent. On a task involving family
relationships, learning is fast and leads to good generalization.
Download: [ps.gz] [pdf]
[home page] [publications]