Abstract
Natural Language Processing (NLP) has known important interest and growth in recent years as may witness the increasing number of publications in different NLP tasks over the world. The primary focus of recent research has been to develop algorithms that process natural language in quantum computers, hence the emergence of new sub-domain called QNLP were proposed. Hence the objective of this paper is to provide to NLP researchers a new vision and way to deal with the NLP problems basing on Quantum computing techniques. The present paper aims to provide a list of existing alternatives and classify them by; implementation on classical or quantum hardware, theoretical or experimental work and representation type of sentence. Our study focuses on the Distributional Compositional Categorical model (DisCoCat), from its mathematical and theoretical demonstration into its experimental results.
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Acknowledgements
I’m extremely grateful to my supervisor Pr.Belhadef for his continuous encouragement to just do my best in this promising domain.
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Bouakba, Y., Belhadef, H. (2023). Quantum Natural Language Processing: A New and Promising Way to Solve NLP Problems. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_17
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DOI: https://doi.org/10.1007/978-3-031-28540-0_17
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