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Quantum methods for managing ambiguity in natural language processing
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DOI：10.1007/s42484-026-00421-5.png)
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Abstract
En 中文
The Categorical Compositional Distributional (DisCoCat) framework models meaning in natural language using the mathematical framework of quantum theory, expressed as formal diagrams. DisCoCat diagrams can be associated with tensor networks and quantum circuits. DisCoCat diagrams have been connected to density matrices in various contexts in Quantum Natural Language Processing (QNLP). Previous use of density matrices in QNLP entails modelling ambiguous words as probability distributions over basic words (e.g., the word queen might mean the reigning queen or the chess piece). In this article, we investigate the use of probability distributions over processes to account for syntactic ambiguity in sentences. The meanings of these sentences are represented by density matrices. We show how to create probability distributions on quantum circuits that represent the meanings of sentences and explain how this approach generalises tasks from the literature. We report on a proof-of-concept experiment to demonstrate the proposed theory.
Keywords:
Quantum computing
Natural language processing
Quantum natural language processing
Quantum machine learning
Computational linguistics
Journal
Q
IF:
4.4
Papers:
436
Citations:
796