# Developments in Sheaf-Theoretic Models of Natural Language Ambiguities

arXiv: https://arxiv.org/abs/2402.04505 · HTML: https://mlsanity.com/paper/2402.04505/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [The Defeat of the Winograd Schema Challenge](https://mlsanity.com/paper/2201.02387/) (2022)
- [On the Quantum-like Contextuality of Ambiguous Phrases](https://mlsanity.com/paper/2107.14589/) (2021)
- [Generalised Winograd Schema and its Contextuality](https://mlsanity.com/paper/2308.16498/) (2023)
- [Snow Queen is Evil and Beautiful: Experimental Evidence for Probabilistic Contextuality in Human Choices](https://mlsanity.com/paper/1711.00418/) (2017)
- [A Model of Anaphoric Ambiguities using Sheaf Theoretic Quantum-like Contextuality and BERT](https://mlsanity.com/paper/2208.05720/) (2022)

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

- [Quantum-Like Contextuality in Large Language Models](https://mlsanity.com/paper/2412.16806/) (2024)

## Abstract (arXiv)

Sheaves are mathematical objects consisting of a base which constitutes a topological space and the data associated with each open set thereof, e.g. continuous functions defined on the open sets. Sheaves have originally been used in algebraic topology and logic. Recently, they have also modelled events such as physical experiments and natural language disambiguation processes. We extend the latter models from lexical ambiguities to discourse ambiguities arising from anaphora. To begin, we calculated a new measure of contextuality for a dataset of basic anaphoric discourses, resulting in a higher proportion of contextual models-82.9%-compared to previous work which only yielded 3.17% contextual models. Then, we show how an extension of the natural language processing challenge, known as the Winograd Schema, which involves anaphoric ambiguities can be modelled on the Bell-CHSH scenario with a contextual fraction of 0.096.