On the universal structure of human lexical semantics
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Abstract (arXiv)
How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides direct access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries. Across languages carefully selected from a phylogenetically and geographically stratified sample of genera, translations of words reveal cases where a particular language uses a single polysemous word to express concepts represented by distinct words in another. We use the frequency of polysemies linking two concepts as a measure of their semantic proximity, and represent the pattern of such linkages by a weighted network. This network is highly uneven and fragmented: certain concepts are far more prone to polysemy than others, and there emerge naturally interpretable clusters loosely connected to each other. Statistical analysis shows such structural properties are consistent across different language groups, largely independent of geography, environment, and literacy. It is therefore possible to conclude the conceptual structure connecting basic vocabulary studied is primarily due to universal features of human cognition and language use.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 2.
8 papers.
- 2023Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphscited by 11
- 2023Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalizationcited by 6
- 2021Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadthcited by 5
- 2022A Data-dependent Approach for High Dimensional (Robust) Wasserstein Alignmentcited by 2
- 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspectivecited by 1
- 2025Measuring Intent Comprehension in LLMscited by 1
- 2023Inference of Partial Colexifications from Multilingual Wordlistscited by 0
- 2025Investigating Lexical Change through Cross-Linguistic Colexification Patternscited by 0
- 2025Investigating Lexical Change through Cross-Linguistic Colexification Patterns15 Oct 2025
- 2025Measuring Intent Comprehension in LLMs19 Jun 2025
- 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspective7 Oct 2024
- 2023Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization19 Oct 2023
- 2023Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification Graphs22 May 2023
- 2023Inference of Partial Colexifications from Multilingual Wordlists1 Feb 2023
- 2022A Data-dependent Approach for High Dimensional (Robust) Wasserstein Alignment7 Sep 2022
- 2021Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth15 Dec 2021