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

CAp 2017 challenge: Twitter Named Entity Recognition