Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese
Kevin Deturck, Pierre Magistry, Bénédicte Diot-Parvaz Ahmad, Ilaine Wang, Damien Nouvel, Hugo Lafayette
Abstract
Transformer language models are now a solid baseline for Named Entity Recognition and can be significantly improved by leveraging complementary resources, either by integrating external knowledge or by annotating additional data. In a preliminary step, this work presents experiments on fine-tuning transformer models. Then, a set of experiments has been conducted with a Wikipedia-based reclassification system. Additionally, we conducted a small annotation campaign on the Farsi language to evaluate the impact of additional data. These two methods with complementary resources showed improvements compared to fine-tuning only.- Anthology ID:
- 2023.semeval-1.306
- Volume:
- Proceedings of 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:
- 2211–2215
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.306
- DOI:
- 10.18653/v1/2023.semeval-1.306
- Cite (ACL):
- Kevin Deturck, Pierre Magistry, Bénédicte Diot-Parvaz Ahmad, Ilaine Wang, Damien Nouvel, and Hugo Lafayette. 2023. Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2211–2215, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese (Deturck et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2023.semeval-1.306.pdf