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

• URL: http://arxiv.org/abs/2104.06048v1
• Date: Tue, 13 Apr 2021 09:23:16 GMT
• Title: Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)
• Authors: Emanuela Boros and Antoine Doucet
• Abstract summary: 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.
• Score: 1.456207068672607
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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