  The paper describes the CAp 2017 challenge. The challenge concerns the
problem of Named Entity Recognition (NER) for tweets written in French. We
first present the data preparation steps we followed for constructing the
dataset released in the framework of the challenge. We begin by demonstrating
why NER for tweets is a challenging problem especially when the number of
entities increases. We detail the annotation process and the necessary
decisions we made. We provide statistics on the inter-annotator agreement, and
we conclude the data description part with examples and statistics for the
data. We, then, describe the participation in the challenge, where 8 teams
participated, with a focus on the methods employed by the challenge
participants and the scores achieved in terms of F$_1$ measure. Importantly,
the constructed dataset comprising $\sim$6,000 tweets annotated for 13 types of
entities, which to the best of our knowledge is the first such dataset in
French, is publicly available at \url{http://cap2017.imag.fr/competition.html} .
