# A Study of Entanglement in a Categorical Framework of Natural Language

arXiv: https://arxiv.org/abs/1405.2874 · HTML: https://mlsanity.com/paper/1405.2874/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2014.

## Built on by (most cited first, 10 of 10)

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- [Kindergarden quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus)](https://mlsanity.com/paper/2102.10984/) (2021)
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- [Rich Event Modeling for Script Event Prediction](https://mlsanity.com/paper/2212.08287/) (2022)
- [PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning](https://mlsanity.com/paper/2404.17877/) (2024)
- [Quantum Methods for Managing Ambiguity in Natural Language Processing](https://mlsanity.com/paper/2504.00040/) (2025)

## Abstract (arXiv)

In both quantum mechanics and corpus linguistics based on vector spaces, the notion of entanglement provides a means for the various subsystems to communicate with each other. In this paper we examine a number of implementations of the categorical framework of Coecke, Sadrzadeh and Clark (2010) for natural language, from an entanglement perspective. Specifically, our goal is to better understand in what way the level of entanglement of the relational tensors (or the lack of it) affects the compositional structures in practical situations. Our findings reveal that a number of proposals for verb construction lead to almost separable tensors, a fact that considerably simplifies the interactions between the words. We examine the ramifications of this fact, and we show that the use of Frobenius algebras mitigates the potential problems to a great extent. Finally, we briefly examine a machine learning method that creates verb tensors exhibiting a sufficient level of entanglement.