A Quantum-Inspired Analysis of Human Disambiguation Processes

TL;DR

This paper explores how quantum-inspired formalism can model human disambiguation in language, outperforming current NLP methods by integrating quantum mechanics concepts to better understand and predict human language processing.

Contribution

It introduces a novel quantum-inspired framework for analyzing linguistic ambiguities and demonstrates its effectiveness in modeling human disambiguation processes.

Findings

Quantum-inspired models outperform traditional NLP in predicting human disambiguation.

Reproduced psycholinguistic results using quantum formalisms.

Framework provides insights into human language processing mechanisms.

Abstract

Formal languages are essential for computer programming and are constructed to be easily processed by computers. In contrast, natural languages are much more challenging and instigated the field of Natural Language Processing (NLP). One major obstacle is the ubiquity of ambiguities. Recent advances in NLP have led to the development of large language models, which can resolve ambiguities with high accuracy. At the same time, quantum computers have gained much attention in recent years as they can solve some computational problems faster than classical computers. This new computing paradigm has reached the fields of machine learning and NLP, where hybrid classical-quantum learning algorithms have emerged. However, more research is needed to identify which NLP tasks could benefit from a genuine quantum advantage. In this thesis, we applied formalisms arising from foundational quantum mechanics, such as contextuality and causality, to study ambiguities arising from linguistics. By doing so, we also reproduced psycholinguistic results relating to the human disambiguation process. These results were subsequently used to predict human behaviour and outperformed current NLP methods.