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Learning Type-Driven Tensor-Based Meaning Representations
Tamara Polajnar, Luana Fagarasan, Stephen Clark
Dec 23, 2013 (modified: Dec 23, 2013)ICLR 2014 conference submissionreaders: everyone
Decision:submitted, no decision
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.
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