The goal of this paper is to establish a new state of the art for French NER by (i) providing a new, easy-to-use UDaligned version of the named entity annotation layer in the FTB and (ii) using this corpus as a training and evaluation dataset for carrying out NER experiments using state-ofthe-art architectures, thereby improving over the previous state of the art in French NER.
by using both FastText embeddings (Bojanowski et al., 2017) and one of the versions of the CamemBERT French neural contextual language model (Martin et al., 2019) within an LSTM-CRF architecture, we can reach an F1-score of 90.25, a 6.5point improvement over the previously state-of-the-art system SEM (Dupont, 2017).