Quantum Methods for Managing Ambiguity in Natural Language Processing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916668347777024 |
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| author | Eisinger, Jurek Gauderis, Ward de Huybrecht, Lin Wiggins, Geraint A. |
| author_facet | Eisinger, Jurek Gauderis, Ward de Huybrecht, Lin Wiggins, Geraint A. |
| contents | 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 more basic words (the word \texttt{queen}, e.g., might mean the reigning queen or the chess piece). In this article, we investigate using 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 conduct an experiment to validate the proposed theory. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_00040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Methods for Managing Ambiguity in Natural Language Processing Eisinger, Jurek Gauderis, Ward de Huybrecht, Lin Wiggins, Geraint A. Computation and Language Artificial Intelligence Quantum Physics I.2 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 more basic words (the word \texttt{queen}, e.g., might mean the reigning queen or the chess piece). In this article, we investigate using 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 conduct an experiment to validate the proposed theory. |
| title | Quantum Methods for Managing Ambiguity in Natural Language Processing |
| topic | Computation and Language Artificial Intelligence Quantum Physics I.2 |
| url | https://arxiv.org/abs/2504.00040 |