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arXiv · 1312.5985

Learning Type-Driven Tensor-Based Meaning Representations

Tamara Polajnar·Luana Fagarasan·Stephen Clark
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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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Tamara Polajnar, Luana Fagarasan, Stephen Clark. 2013-12-20. Learning Type-Driven Tensor-Based Meaning Representations. https://arxiv.org/abs/1312.5985

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