Dependency distance: A new perspective on syntactic patterns in natural languages |
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| Affiliation: | 1. Centre for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou, 510420, China;2. Department of Linguistics, Zhejiang University, Hangzhou, 310058, China;3. School of Foreign Studies, Anhui Jianzhu University, Hefei 230601, China;4. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China;1. Complexity & Quantitative Linguistics Lab, LARCA Research Group, Departament de Ciències de la Computació, Universitat Politècnica de Catalunya, Campus Nord, Edifici Omega, Jordi Girona Salgado 1-3, 08034, Barcelona, Catalonia, Spain;2. Universidade da Coruña, FASTPARSE Lab, LyS Research Group, Departamento de Computación, Facultade de Informática, Elviña, 15071 A Coruña, Spain;3. Logic and Programming, LOGPROG Research Group, Departament de Ciències de la Computació, Universitat Politècnica de Catalunya, Campus Nord, Edifici Omega, Jordi Girona Salgado 1-3, 08034 Barcelona, Catalonia, Spain;1. Department of Humanities and Social Sciences, IIT Delhi, Hauz Khas, New Delhi 110016, India;2. Department of Linguistics, The Ohio State University, Oxley Hall, 1712 Neil Ave., Columbus, OH 43210, USA;1. School of Foreign Studies, Anhui Jianzhu University, Hefei 230601, China;2. Department of Linguistics, Zhejiang University, Hangzhou, 310058, China;3. Centre for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou, 510420, China;4. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China |
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| Abstract: | Dependency distance, measured by the linear distance between two syntactically related words in a sentence, is generally held as an important index of memory burden and an indicator of syntactic difficulty. Since this constraint of memory is common for all human beings, there may well be a universal preference for dependency distance minimization (DDM) for the sake of reducing memory burden. This human-driven language universal is supported by big data analyses of various corpora that consistently report shorter overall dependency distance in natural languages than in artificial random languages and long-tailed distributions featuring a majority of short dependencies and a minority of long ones. Human languages, as complex systems, seem to have evolved to come up with diverse syntactic patterns under the universal pressure for dependency distance minimization. However, there always exist a small number of long-distance dependencies in natural languages, which may reflect some other biological or functional constraints. Language system may adapt itself to these sporadic long-distance dependencies. It is these universal constraints that have shaped such a rich diversity of syntactic patterns in human languages. |
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| Keywords: | Dependency distance Language universal Syntactic patterns |
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