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Abstract:
We present Linear Relational Embedding (LRE), a new method of
learning a distributed representation of concepts from data
consisting of instances of relations between given concepts. Its
final goal is to be able to generalize, i.e. infer new instances
of these relations among the concepts. On a task involving family
relationships we show that LRE can generalize better than any
previously published method. We then show how LRE can be used
effectively to find compact distributed representations for
variable-sized recursive data structures, such as trees and
lists.
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