A probabilistic framework for analysing the compositionality of conceptual combinations
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Abstract (arXiv)
Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilized in everyday language. This article provides a novel probabilistic framework for assessing whether the semantics of conceptual combinations are compositional, and so can be considered as a function of the semantics of the constituent concepts, or not. While the systematicity and productivity of language provide a strong argument in favor of assuming compositionality, this very assumption is still regularly questioned in both cognitive science and philosophy. Additionally, the principle of semantic compositionality is underspecified, which means that notions of both "strong" and "weak" compositionality appear in the literature. Rather than adjudicating between different grades of compositionality, the framework presented here contributes formal methods for determining a clear dividing line between compositional and non-compositional semantics. In addition, we suggest that the distinction between these is contextually sensitive. Utilizing formal frameworks developed for analyzing composite systems in quantum theory, we present two methods that allow the semantics of conceptual combinations to be classified as "compositional" or "non-compositional". Compositionality is first formalised by factorising the joint probability distribution modeling the combination, where the terms in the factorisation correspond to individual concepts. This leads to the necessary and sufficient condition for the joint probability distribution to exist. A failure to meet this condition implies that the underlying concepts cannot be modeled in a single probability space when considering their combination, and the combination is thus deemed "non-compositional". The formal analysis methods are demonstrated by applying them to an empirical study of twenty-four non-lexicalised conceptual combinations.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2013.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 2; 2025: 1.
8 papers.
- 2021On the Quantum-like Contextuality of Ambiguous Phrasescited by 6
- 2021Quantum Mathematics in Artificial Intelligencecited by 5
- 2025A quantum semantic framework for natural language processingcited by 5
- 2022The Causal Structure of Semantic Ambiguitiescited by 1
- 2023Quantum Circuit Components for Cognitive Decision-Makingcited by 1
- 2024Coupling quantum-like cognition with the neuronal networks within generalized probability theorycited by 1
- 2022An Extension Of Combinatorial Contextuality For Cognitive Protocolscited by 0
- 2024Qualia and the Formal Structure of Meaningcited by 0
- 2025A quantum semantic framework for natural language processing11 Jun 2025
- 2024Coupling quantum-like cognition with the neuronal networks within generalized probability theory29 Oct 2024
- 2024Qualia and the Formal Structure of Meaning2 May 2024
- 2023Quantum Circuit Components for Cognitive Decision-Making6 Feb 2023
- 2022The Causal Structure of Semantic Ambiguities14 Jun 2022
- 2022An Extension Of Combinatorial Contextuality For Cognitive Protocols15 Feb 2022
- 2021On the Quantum-like Contextuality of Ambiguous Phrases19 Jul 2021
- 2021Quantum Mathematics in Artificial Intelligence12 Jan 2021