papers AI Learner
The Github is limit! Click to go to the new site.

Complexity-entropy analysis at different levels of organization in written language

2019-03-14
E. Estevez-Rams, A. Mesa Rodriguez, D. Estevez-Moya

Abstract

Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, unpredictability is an essential element of natural language. Here we present the use of entropic measures to assert the balance between predictability and surprise in written text. In short, it is possible to measure innovation and context preservation in a document. It is shown that this can also be done at the different levels of organization of a text. The type of analysis presented is reasonably general, and can also be used to analyze the same balance in other complex messages such as DNA, where a hierarchy of organizational levels are known to exist.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1903.07416

PDF

http://arxiv.org/pdf/1903.07416


Comments

Content