Ferrer-I-Cancho Ramon, Gómez-Rodríguez Carlos, Esteban Juan Luis, Alemany-Puig Lluís
Complexity and 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.
Universidade da Coruña, CITIC, FASTPARSE Lab, LyS Research Group, Departamento de Ciencias de la Computación y Tecnologías de la Información, Facultade de Informática, Elviña, 15071, A Coruña, Spain.
Phys Rev E. 2022 Jan;105(1-1):014308. doi: 10.1103/PhysRevE.105.014308.
It is often stated that human languages, as other biological systems, are shaped by cost-cutting pressures but, to what extent? Attempts to quantify the degree of optimality of languages by means of an optimality score have been scarce and focused mostly on English. Here we recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network where the vertices are words, arcs indicate syntactic dependencies, and the space is defined by the linear order of the words in the sentence. We introduce a score to quantify the cognitive pressure to reduce the distance between linked words in a sentence. The analysis of sentences from 93 languages representing 19 linguistic families reveals that half of languages are optimized to a 70% or more. The score indicates that distances are not significantly reduced in a few languages and confirms two theoretical predictions: that longer sentences are more optimized and that distances are more likely to be longer than expected by chance in short sentences. We present a hierarchical ranking of languages by their degree of optimization. The score has implications for various fields of language research (dependency linguistics, typology, historical linguistics, clinical linguistics, and cognitive science). Finally, the principles behind the design of the score have implications for network science.
