@inproceedings{deturck-etal-2023-ertim,
    title = "Ertim at {S}em{E}val-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for {NER} in {F}arsi, {E}nglish, {F}rench and {C}hinese",
    author = "Deturck, Kevin  and
      Magistry, Pierre  and
      Diot-Parvaz Ahmad, B{\'e}n{\'e}dicte  and
      Wang, Ilaine  and
      Nouvel, Damien  and
      Lafayette, Hugo",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.306",
    doi = "10.18653/v1/2023.semeval-1.306",
    pages = "2211--2215",
    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.",
}

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.
Deturck, Kevin Magistry, Pierre Diot-Parvaz Ahmad, B{\'e}n{\'e}dicte Wang, Ilaine Nouvel, Damien Lafayette, Hugo
Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese