# An ERNIE-Based Joint Model for Chinese Named Entity Recognition
## 6 Conclusions  
In this paper, we enhance the performance of Chinese NER through an ERNIE-based joint model called ERNIE-Joint. We choose ERNIE as the pre-training model we used because of the knowledge masking strategy it has during pre-training procedure. Knowledge masking strategy can obtain the prior semantic knowledge of entities or phrases, which is more suitable for Chinese NER because there is no segmentation between Chinese characters, and BERT can only obtain character-level representations. Moreover, The ERNIE-Joint, which is a joint training model, can utilize both the sentence-level and token-level features when performing the NER task through a unique cost function. In order to use the raw NER dataset for joint training and avoid additional annotations, text classification task is performed according to the number of entities in sentences. The experiments are conducted on two datasets--MSRA-NER and Weibo. These datasets contain Chinese news data and Chinese social media data, respectively. The results demonstrate that ERNIE-Joint not only outperforms BERT and ERNIE but also obtained the SOTA results on both datasets.  
For future work, firstly, we will test the performance of ERNIE-Joint in specific domains, such as sports medicine. Experiments will be carried out on a sports medicine related dataset labelled by ourselves. Secondly, we will test ERNIE-Joint on a multilingual dataset and observe its performance in other languages. Thirdly, In order to build a Chinese KG from unstructured documents, we need to extract the relationship of entities recognized by ERNIE-Joint. Considering that the RE can also be regarded as a classification task, we will try to use ERNIE-Joint for this task.  
Conceptualization, Y.W. and Y.S.; methodology, Y.W.; software, Y.W.; validation, Y.W., L.G. and Y.X.; formal analysis, Z.M.; investigation, Y.W.; resources, L.G.; data curation, Y.X.; writing--original draft preparation, Y.W.; writing--review and editing, Y.S.; supervision, Y.S.; project administration, Z.M.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the major special project of Anhui Science and Technology Department grant number 18030801133, and Science and Technology Service Network Initiative grant number KFJ-STS-ZDTP-079.  
The authors would like to thank the AIStudio platform for providing computing resources.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BERT & Bidirectional Encoder Representations from Transformers \\ BiGRU & Bidirectional Gate Recurrent Unit \\ BiLM & Bidirectional Language Model \\ BiLSTM & Bidirectional Long Short-Term Memory \\ CAN & Convolutional Attention Network \\ CNN & Convolutional Neural Networks \\ CRF & Conditional Random Field \\ ERNIE & Enhanced Representation through kNowledge IntEgration \\ ELMO & Embeddings from Language Models \\ KG & Knowledge Graph \\ MSRA & Microsoft Research Asia \\ NER & Named Entity Recognition \\ NLP & Natural Language Processing \\ RE & Relation Extraction \\ SOTA & State-of-the-art \\ \end{tabular}