Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese
Kévin Deturck, Pierre Magistry, Bénédicte Diot-Parvaz Ahmad, Ilaine Wang, Damien Nouvel, Hugo Lafayette · 2023
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.