A Quantum-Inspired Analysis of Human Disambiguation Processes
This work addresses the problem of improving natural language processing for researchers and practitioners by offering a novel approach to disambiguation, though it appears incremental as it builds on existing quantum-inspired methods.
The paper tackled the challenge of natural language ambiguities by applying quantum mechanics formalisms like contextuality and causality to linguistics, which reproduced psycholinguistic results and outperformed current NLP methods in predicting human behavior.
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.