  This paper summarizes the participation of the Laboratoire Informatique,
Image et Interaction (L3i laboratory) of the University of La Rochelle in the
Recognizing Ultra Fine-grained Entities (RUFES) track within the Text Analysis
Conference (TAC) series of evaluation workshops. Our participation relies on
two neural-based models, one based on a pre-trained and fine-tuned language
model with a stack of Transformer layers for fine-grained entity extraction and
one out-of-the-box model for within-document entity coreference. We observe
that our approach has great potential in increasing the performance of
fine-grained entity recognition. Thus, the future work envisioned is to enhance
the ability of the models following additional experiments and a deeper
analysis of the results.
