VTCC-NER at SemEval-2023 Task 6: An Ensemble Pre-trained Language Models for Named Entity Recognition

Quang-Minh Tran, Xuan-Dung Doan


Abstract
We propose an ensemble method that combines several pre-trained language models to enhance entity recognition in legal text. Our approach achieved a 90.9873% F1 score on the private test set, ranking 2nd on the leaderboard for SemEval 2023 Task 6, Subtask B - Legal Named Entities Extraction.
Anthology ID:
2023.semeval-1.56
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
415–419
Language:
URL:
https://aclanthology.org/2023.semeval-1.56
DOI:
10.18653/v1/2023.semeval-1.56
Bibkey:
Cite (ACL):
Quang-Minh Tran and Xuan-Dung Doan. 2023. VTCC-NER at SemEval-2023 Task 6: An Ensemble Pre-trained Language Models for Named Entity Recognition. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 415–419, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
VTCC-NER at SemEval-2023 Task 6: An Ensemble Pre-trained Language Models for Named Entity Recognition (Tran & Doan, SemEval 2023)
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PDF:
https://aclanthology.org/2023.semeval-1.56.pdf