An ERNIE-Based Joint Model for Chinese Named Entity Recognition
文献类型：期刊论文
| 作者 | Wang, Yu1,2 ; Sun, Yining1,2; Ma, Zuchang1; Gao, Lisheng1; Xu, Yang1
|
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2020-08-01 |
| 卷号 | 10 |
| 关键词 | joint training named entity recognition pre-training models ERNIE BERT |
| DOI | 10.3390/app10165711 |
| 通讯作者 | Sun, Yining(ynsun@iim.ac.cn) |
| 英文摘要 | Named Entity Recognition (NER) is the fundamental task for Natural Language Processing (NLP) and the initial step in building a Knowledge Graph (KG). Recently, BERT (Bidirectional Encoder Representations from Transformers), which is a pre-training model, has achieved state-of-the-art (SOTA) results in various NLP tasks, including the NER. However, Chinese NER is still a more challenging task for BERT because there are no physical separations between Chinese words, and BERT can only obtain the representations of Chinese characters. Nevertheless, the Chinese NER cannot be well handled with character-level representations, because the meaning of a Chinese word is quite different from that of the characters, which make up the word. ERNIE (Enhanced Representation through kNowledge IntEgration), which is an improved pre-training model of BERT, is more suitable for Chinese NER because it is designed to learn language representations enhanced by the knowledge masking strategy. However, the potential of ERNIE has not been fully explored. ERNIE only utilizes the token-level features and ignores the sentence-level feature when performing the NER task. In this paper, we propose the ERNIE-Joint, which is a joint model based on ERNIE. The ERNIE-Joint can utilize both the sentence-level and token-level features by joint training the NER and text classification tasks. In order to use the raw NER datasets for joint training and avoid additional annotations, we perform the text classification task according to the number of entities in the 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 the ERNIE-Joint not only outperforms BERT and ERNIE but also achieves the SOTA results on both datasets. |
| 资助项目 | major special project of Anhui Science and Technology Department[18030801133] ; Science and Technology Service Network Initiative[KFJ-STS-ZDTP-079] |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000567143300001 |
| 出版者 | MDPI |
| 资助机构 | major special project of Anhui Science and Technology Department ; Science and Technology Service Network Initiative |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/104066]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Sun, Yining |
| 作者单位 | 1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, AnHui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yu,Sun, Yining,Ma, Zuchang,et al. An ERNIE-Based Joint Model for Chinese Named Entity Recognition[J]. APPLIED SCIENCES-BASEL,2020,10. |
| APA | Wang, Yu,Sun, Yining,Ma, Zuchang,Gao, Lisheng,&Xu, Yang.(2020).An ERNIE-Based Joint Model for Chinese Named Entity Recognition.APPLIED SCIENCES-BASEL,10. |
| MLA | Wang, Yu,et al."An ERNIE-Based Joint Model for Chinese Named Entity Recognition".APPLIED SCIENCES-BASEL 10(2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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