Quantifying French Document Complexity

TL;DR

This paper introduces a methodology for measuring the complexity of French documents using a new corpus and various metrics, providing a general-purpose complexity assessment tool for French texts.

Contribution

It presents a novel approach combining a new corpus and multiple metrics to quantify French document complexity, filling a gap in non-English text complexity measurement.

Findings

The methodology effectively measures French text complexity.

Different learning algorithms reveal key text characteristics influencing complexity.

The approach is adaptable to diverse French texts and corpora.

Abstract

Measuring a document's complexity level is an open challenge, particularly when one is working on a diverse corpus of documents rather than comparing several documents on a similar topic or working on a language other than English. In this paper, we define a methodology to measure the complexity of French documents, using a new general and diversified corpus of texts, the "French Canadian complexity level corpus", and a wide range of metrics. We compare different learning algorithms to this task and contrast their performances and their observations on which characteristics of the texts are more significant to their complexity. Our results show that our methodology gives a general-purpose measurement of text complexity in French.