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://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.56/
- DOI:
- 10.18653/v1/2023.semeval-1.56
- 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)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2023.semeval-1.56.pdf