Abstract
This paper introduces our system for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) competition. Our team focused on the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. To achieve our goal, we utilized transfer learning by fine-tuning pre-trained language models (PLMs) on the competition dataset. Our approach involved combining a BERT-based PLM with external knowledge to provide additional context to the model. In this report, we present our findings and results.- Anthology ID:
- 2023.semeval-1.206
- 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:
- 1498–1501
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.206
- DOI:
- 10.18653/v1/2023.semeval-1.206
- Cite (ACL):
- Caleb Martin, Huichen Yang, and William Hsu. 2023. KDDIE at SemEval-2023 Task 2: External Knowledge Injection for Named Entity Recognition. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1498–1501, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- KDDIE at SemEval-2023 Task 2: External Knowledge Injection for Named Entity Recognition (Martin et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.semeval-1.206.pdf