@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Ertim at SemEval-2023 Task 2: Fine-tuning of Transformer Language Models and External Knowledge Leveraging for NER in Farsi, English, French and Chinese
%A Deturck, Kevin
%A Magistry, Pierre
%A Diot-Parvaz Ahmad, Bénédicte
%A Wang, Ilaine
%A Nouvel, Damien
%A Lafayette, Hugo
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F deturck-etal-2023-ertim
%X 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.
%R 10.18653/v1/2023.semeval-1.306
%U https://aclanthology.org/2023.semeval-1.306
%U https://doi.org/10.18653/v1/2023.semeval-1.306
%P 2211-2215
Markdown (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](https://aclanthology.org/2023.semeval-1.306) (Deturck et al., SemEval 2023)
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.