  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.
