Abstract
This paper presents D2KLab’s system used for the shared task of “Multilingual Complex Named Entity Recognition (MultiCoNER II)”, as part of SemEval 2023 Task 2. The system relies on a fine-tuned transformer based language model for extracting named entities. In addition to the architecture of the system, we discuss our results and observations.- Anthology ID:
- 2023.semeval-1.115
- 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:
- 836–840
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.115
- DOI:
- 10.18653/v1/2023.semeval-1.115
- Cite (ACL):
- Thibault Ehrhart, Julien Plu, and Raphael Troncy. 2023. D2KLab at SemEval-2023 Task 2: Leveraging T-NER to Develop a Fine-Tuned Multilingual Model for Complex Named Entity Recognition. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 836–840, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- D2KLab at SemEval-2023 Task 2: Leveraging T-NER to Develop a Fine-Tuned Multilingual Model for Complex Named Entity Recognition (Ehrhart et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.semeval-1.115.pdf