Reference : Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Enti...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/45217
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
English
[en] Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
Lothritz, Cedric mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Allix, Kevin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Veiber, Lisa mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Dec-2020
Proceedings of the 28th International Conference on Computational Linguistics
3750–3760
Yes
28th International Conference on Computational Linguistics
08.12-13.12
[en] Natural Language Processing ; fine-grained Named Entity Recognition ; Transformers
[en] 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.
http://hdl.handle.net/10993/45217

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