# Learning Type-Driven Tensor-Based Meaning Representations

arXiv: https://arxiv.org/abs/1312.5985 · HTML: https://mlsanity.com/paper/1312.5985/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2013.

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning for Finance](https://mlsanity.com/paper/2109.04298/) (2021)

## Abstract (arXiv)

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.