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.
Article ID: 2022L36
Publisher: Canadian Artificial Intelligence Association