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

## TL;DR

This paper introduces entropic measures to analyze the balance between predictability and surprise in written language, assessing complexity at various organizational levels and applicable to other complex systems like DNA.

## Contribution

It presents a novel entropic analysis method to quantify innovation and context preservation across different levels of language organization.

## Key findings

- Entropic measures effectively characterize language complexity.
- Analysis can be applied to other complex hierarchical messages.
- Balance between predictability and surprise is quantifiable.

## 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.