Large-scale evidence of dependency length minimization in 37 languages
Open accessAbstract
Significance: We provide the first large-scale, quantitative, cross-linguistic evidence for a universal syntactic property of languages: that dependency lengths are shorter than chance. Our work supports long-standing ideas that speakers prefer word orders with short dependency lengths and that languages do not enforce word orders with long dependency lengths. Dependency length minimization is well motivated because it allows for more efficient parsing and generation of natural language. Over the last 20 y, the hypothesis of a pressure to minimize dependency length has been invoked to explain many of the most striking recurring properties of languages. Our broad-coverage findings support those explanations.
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Human language dependencies tend to be much shorter than expected by chance (dependency length minimization).
“It has been observed in human language that dependencies tend to be much shorter than expected by chance, a phenomenon known as dependency length minimization (Ferrer-i-Cancho 2004; Futrell et al. 2015).”
From Contrasting Linguistic Patterns in Human and LLM-Generated News Text (2024) · cites this paper as Futrell et al. 2015
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- Authors
- Futrell, R., Mahowald, K., & Gibson, E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-08-03
- Volume
- 112
- Issue
- 33
- Pages
- 10336-10341
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4547262
- OpenAlex
- W2114719613
- Cited by
- 455
- References
- 61
- Citations by year
- 2026: 38 · 2025: 45 · 2024: 30 · 2023: 40 · 2022: 74 · 2021: 42 · 2020: 40 · 2019: 40 · 2018: 46 · 2017: 29 · 2016: 21 · 2015: 9 · 2014: 1
- FWCI
- 234.50
- Type
- journal article
- Language
- en
- Update notices
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- U.S. Department of Defense
- National Defense Science and Engineering Graduate
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- cross-linguistic variation, language universals, parsed corpora, sentence processing, computational models of language, human information processing, linguistic typology
- MeSH terms
- Humans, Language, Linguistics, Models, Theoretical, Regression Analysis, Comprehension
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