Abstract
Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities.Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.- Anthology ID:
- 2023.semeval-1.62
- Volume:
- Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 453–456
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.62
- DOI:
- Cite (ACL):
- Rahul Mehta and Vasudeva Varma. 2023. LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa. In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 453–456, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa (Mehta & Varma, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.semeval-1.62.pdf