what evaluation metrics did they use?
Precision 
Recall
F1
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.
Named Entity Recognition (NER) is a basic and important task in Natural Language Processing (NLP). It aims to recognize and classify named entities, such as person names and location namesBIBREF0. Extracting named entities from unstructured data can benefit many NLP tasks, for example Knowledge Graph (KG), Decision-making Support System (DSS), and Question Answering system. Researchers used rule-based and machine learning methods for the NER in the early yearsBIBREF1BIBREF2. Recently, with the development of deep learning, deep neural networks have improved the performance of NER tasksBIBREF3BIBREF4. However, it may still be inefficient to use deep neural networks because the performance of these methods depends on the quality of labeled data in training sets while creating annotations for unstructured data is especially difficultBIBREF5. Therefore, researchers hope to find an efficient method to extract semantic and syntactic knowledge from a large amount of unstructured data, which is also unlabeled. Then, apply the semantic and syntactic knowledge to improve the performance of NLP task effectively. Recent theoretical developments have revealed that word embeddings have shown to be effective for improving many NLP tasks. The Word2Vec and Glove models represent a word as a word embedding, where similar words have similar word embeddingsBIBREF6. However, the Word2Vec and Glove models can not solve the problem of polysemy. Researchers have proposed some pre-training models, such as BERT, ERNIE, and RoBERTa, to learn contextualized word embeddings from unstructured text corpusBIBREF7BIBREF8BIBREF9. These models not only solve the problem of polysemy but also obtain more accurate word representations. Therefore, researchers pay more attention to how to apply these pre-training models to improve the performance of NLP tasks. The purpose of this paper is to introduce the structure and pre-training tasks of four common pre-trained models (BERT, ERNIE, ERNIE2.0-tiny, RoBERTa), and how to apply these models to a NER task by fine-tuning. Moreover, we also conduct experiments on the MSRA-2006 dataset to test the effects of different pre-training models on the NER task, and discuss the reasons for these results from the model architecture and pre-training tasks respectively.