Abstract
In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.- Anthology ID:
- 2022.semeval-1.210
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1531–1535
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.210
- DOI:
- 10.18653/v1/2022.semeval-1.210
- Cite (ACL):
- Caleb Martin, Huichen Yang, and William Hsu. 2022. KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1531–1535, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition (Martin et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.semeval-1.210.pdf