Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)
This work addresses entity recognition for specific domains, but it is incremental as it builds on existing Transformer methods without introducing major innovations.
The paper tackled the problem of recognizing ultra fine-grained entities by using two neural-based models, including a pre-trained and fine-tuned Transformer model, and reported that the approach has great potential to increase performance in fine-grained entity recognition.
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.