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    <abstract>Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task. In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF). Furthermore, we apply each model to a multitude of distinct domains. We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score. Furthermore, we find that the choice of domains significantly influenced the performance regardless of the respective data size or the model chosen.</abstract>
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%0 Conference Proceedings
%T Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
%A Lothritz, Cedric
%A Allix, Kevin
%A Veiber, Lisa
%A Bissyandé, Tegawendé F.
%A Klein, Jacques
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F lothritz-etal-2020-evaluating
%X Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task. In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF). Furthermore, we apply each model to a multitude of distinct domains. We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score. Furthermore, we find that the choice of domains significantly influenced the performance regardless of the respective data size or the model chosen.
%R 10.18653/v1/2020.coling-main.334
%U https://aclanthology.org/2020.coling-main.334/
%U https://doi.org/10.18653/v1/2020.coling-main.334
%P 3750-3760

Markdown (Informal)

[Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition](https://aclanthology.org/2020.coling-main.334/) (Lothritz et al., COLING 2020)

• Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition (Lothritz et al., COLING 2020)

ACL

• Cedric Lothritz, Kevin Allix, Lisa Veiber, Tegawendé F. Bissyandé, and Jacques Klein. 2020. Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3750–3760, Barcelona, Spain (Online). International Committee on Computational Linguistics.