Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)

TL;DR

This paper presents neural-based Transformer models for recognizing ultra fine-grained entities and coreference, demonstrating potential improvements in fine-grained entity recognition performance.

Contribution

The paper introduces Transformer-based neural models specifically designed for ultra fine-grained entity recognition and coreference within documents.

Findings

Models show promising potential in improving fine-grained entity recognition.

Transformer-based approaches outperform baseline methods.

Future work includes further experiments and deeper analysis.

Abstract

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.