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Universals of word order reflect optimization of grammars for efficient communication
Copyright policy )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 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.
Significance Human languages share many grammatical properties. We show that some of these properties can be explained by the need for languages to offer efficient communication between humans given our cognitive constraints. Grammars of languages seem to find a balance between two communicative pressures: to be simple enough to allow the speaker to easily produce sentences, but complex enough to be unambiguous to the hearer, and this balance explains well-known word-order generalizations across our sample of 51 varied languages. Our results offer quantitative and computational evidence that language structure is dynamically shaped by communicative and cognitive pressures.
- University of California, Irvine United States
- University of New Mexico United States
- Department of Linguistics University of California United States
- Stanford University United States
- University of California System United States
Microsoft Academic Graph classification: Computer science media_common.quotation_subject computer.software_genre Rule-based machine translation Subject (grammar) media_common business.industry Ambiguity Problem of universals Artificial intelligence Computational linguistics business computer Word (computer architecture) Natural language processing Linguistic universal Word order
Neural Networks, Generalization, Social Sciences, Language Development, Generalization, Psychological, computational linguistics, Computer, Cognition, Clinical Research, language universals, Humans, Language, Multidisciplinary, Communication, language processing, Linguistics, Psychological and Cognitive Sciences, Psychological, Neural Networks, Computer
Neural Networks, Generalization, Social Sciences, Language Development, Generalization, Psychological, computational linguistics, Computer, Cognition, Clinical Research, language universals, Humans, Language, Multidisciplinary, Communication, language processing, Linguistics, Psychological and Cognitive Sciences, Psychological, Neural Networks, Computer
Microsoft Academic Graph classification: Computer science media_common.quotation_subject computer.software_genre Rule-based machine translation Subject (grammar) media_common business.industry Ambiguity Problem of universals Artificial intelligence Computational linguistics business computer Word (computer architecture) Natural language processing Linguistic universal Word order
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).27 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).27 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!- University of California, Irvine United States
- University of New Mexico United States
- Department of Linguistics University of California United States
- Stanford University United States
- University of California System United States
- Stanford University United States
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.
Significance Human languages share many grammatical properties. We show that some of these properties can be explained by the need for languages to offer efficient communication between humans given our cognitive constraints. Grammars of languages seem to find a balance between two communicative pressures: to be simple enough to allow the speaker to easily produce sentences, but complex enough to be unambiguous to the hearer, and this balance explains well-known word-order generalizations across our sample of 51 varied languages. Our results offer quantitative and computational evidence that language structure is dynamically shaped by communicative and cognitive pressures.