Measuring Conceptual Entanglement in Collections of Documents
This work addresses the challenge of modeling non-compositional conceptual phenomena in NLP, offering insights for conceptual modeling and language processing, though it appears incremental as it applies existing quantum cognition ideas to a new domain.
The paper tackled the problem of detecting conceptual entanglement, a quantum cognition phenomenon, in natural language documents by analyzing word traces as indicators of entangled concepts, finding a strong link between conceptual entanglement and language structure.
Conceptual entanglement is a crucial phenomenon in quantum cognition because it implies that classical probabilities cannot model non--compositional conceptual phenomena. While several psychological experiments have been developed to test conceptual entanglement, this has not been explored in the context of Natural Language Processing. In this paper, we apply the hypothesis that words of a document are traces of the concepts that a person has in mind when writing the document. Therefore, if these concepts are entangled, we should be able to observe traces of their entanglement in the documents. In particular, we test conceptual entanglement by contrasting language simulations with results obtained from a text corpus. Our analysis indicates that conceptual entanglement is strongly linked to the way in which language is structured. We discuss the implications of this finding in the context of conceptual modeling and of Natural Language Processing.