# A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT

arXiv: https://arxiv.org/abs/2208.05720 · HTML: https://mlsanity.com/paper/2208.05720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [On the Quantum-like Contextuality of Ambiguous Phrases](https://mlsanity.com/paper/2107.14589/) (2021)
- [On Contextuality in Behavioral Data](https://mlsanity.com/paper/1508.04751/) (2015)

## Built on by (most cited first, 3 of 3)

- [Generalised Winograd Schema and its Contextuality](https://mlsanity.com/paper/2308.16498/) (2023)
- [Quantum-Like Contextuality in Large Language Models](https://mlsanity.com/paper/2412.16806/) (2024)
- [Developments in Sheaf-Theoretic Models of Natural Language Ambiguities](https://mlsanity.com/paper/2402.04505/) (2024)

## Abstract (arXiv)

Ambiguities of natural language do not preclude us from using it and context helps in getting ideas across. They, nonetheless, pose a key challenge to the development of competent machines to understand natural language and use it as humans do. Contextuality is an unparalleled phenomenon in quantum mechanics, where different mathematical formalisms have been put forwards to understand and reason about it. In this paper, we construct a schema for anaphoric ambiguities that exhibits quantum-like contextuality. We use a recently developed criterion of sheaf-theoretic contextuality that is applicable to signalling models. We then take advantage of the neural word embedding engine BERT to instantiate the schema to natural language examples and extract probability distributions for the instances. As a result, plenty of sheaf-contextual examples were discovered in the natural language corpora BERT utilises. Our hope is that these examples will pave the way for future research and for finding ways to extend applications of quantum computing to natural language processing.