  Named Entity Recognition (NER) involves the identification and classification
of named entities in unstructured text into predefined classes. NER in
languages with limited resources, like French, is still an open problem due to
the lack of large, robust, labelled datasets. In this paper, we propose a
transformer-based NER approach for French using adversarial adaptation to
similar domain or general corpora for improved feature extraction and better
generalization. We evaluate our approach on three labelled datasets and show
that our adaptation framework outperforms the corresponding non-adaptive models
for various combinations of transformer models, source datasets and target
corpora.
