The Quantum Challenge in Concept Theory and Natural Language Processing
This work addresses the challenge of improving natural language processing and information retrieval by applying quantum theory to model human cognition, though it appears incremental as it builds on two decades of prior research.
The paper overviews a quantum cognition approach that models human concepts and language using quantum structures, specifically a Fock-Hilbert space framework to handle concept combinations, and proposes a meaning-based information technology method that reconstructs entities from texts.
The mathematical formalism of quantum theory has been successfully used in human cognition to model decision processes and to deliver representations of human knowledge. As such, quantum cognition inspired tools have improved technologies for Natural Language Processing and Information Retrieval. In this paper, we overview the quantum cognition approach developed in our Brussels team during the last two decades, specifically our identification of quantum structures in human concepts and language, and the modeling of data from psychological and corpus-text-based experiments. We discuss our quantum-theoretic framework for concepts and their conjunctions/disjunctions in a Fock-Hilbert space structure, adequately modeling a large amount of data collected on concept combinations. Inspired by this modeling, we put forward elements for a quantum contextual and meaning-based approach to information technologies in which 'entities of meaning' are inversely reconstructed from texts, which are considered as traces of these entities' states.