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ACL
2015

Low-Rank Tensors for Verbs in Compositional Distributional Semantics

7 years 11 months ago
Low-Rank Tensors for Verbs in Compositional Distributional Semantics
Several compositional distributional semantic methods use tensors to model multi-way interactions between vectors. Unfortunately, the size of the tensors can make their use impractical in large-scale implementations. In this paper, we investigate whether we can match the performance of full tensors with low-rank approximations that use a fraction of the original number of parameters. We investigate the effect of low-rank tensors on the transitive verb construction where the verb is a third-order tensor. The results show that, while the low-rank tensors require about two orders of magnitude fewer parameters per verb, they achieve performance comparable to, and occasionally surpassing, the unconstrained-rank tensors on sentence similarity and verb disambiguation tasks.
Daniel Fried, Tamara Polajnar, Stephen Clark
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Daniel Fried, Tamara Polajnar, Stephen Clark
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