Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task to extract entities from unstructured data. The previous methods for NER were based on machine learning or deep learning. Recently, pre-training models have significantly improved performance on multiple NLP tasks. In this paper, firstly, we introduce the architecture and pre-training tasks of four common pre-training models: BERT, ERNIE, ERNIE2.0-tiny, and RoBERTa. Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task. The experiment results showed that RoBERTa achieved state-of-the-art results on the MSRA-2006 dataset.
@article{arxiv.2002.08902,
title = {Application of Pre-training Models in Named Entity Recognition},
author = {Yu Wang and Yining Sun and Zuchang Ma and Lisheng Gao and Yang Xu and Ting Sun},
journal= {arXiv preprint arXiv:2002.08902},
year = {2020}
}