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Universals of word order reflect optimization of grammars for efficient communication
The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties—those related to word order—result from a process of optimization for...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007543/ https://www.ncbi.nlm.nih.gov/pubmed/31964811 http://dx.doi.org/10.1073/pnas.1910923117 |
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author | Hahn, Michael Jurafsky, Dan Futrell, Richard |
author_facet | Hahn, Michael Jurafsky, Dan Futrell, Richard |
author_sort | Hahn, Michael |
collection | PubMed |
description | The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties—those related to word order—result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages. |
format | Online Article Text |
id | pubmed-7007543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-70075432020-02-18 Universals of word order reflect optimization of grammars for efficient communication Hahn, Michael Jurafsky, Dan Futrell, Richard Proc Natl Acad Sci U S A Social Sciences The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties—those related to word order—result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages. National Academy of Sciences 2020-02-04 2020-01-21 /pmc/articles/PMC7007543/ /pubmed/31964811 http://dx.doi.org/10.1073/pnas.1910923117 Text en Copyright © 2020 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Social Sciences Hahn, Michael Jurafsky, Dan Futrell, Richard Universals of word order reflect optimization of grammars for efficient communication |
title | Universals of word order reflect optimization of grammars for efficient communication |
title_full | Universals of word order reflect optimization of grammars for efficient communication |
title_fullStr | Universals of word order reflect optimization of grammars for efficient communication |
title_full_unstemmed | Universals of word order reflect optimization of grammars for efficient communication |
title_short | Universals of word order reflect optimization of grammars for efficient communication |
title_sort | universals of word order reflect optimization of grammars for efficient communication |
topic | Social Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007543/ https://www.ncbi.nlm.nih.gov/pubmed/31964811 http://dx.doi.org/10.1073/pnas.1910923117 |
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