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| 基于知识库实体增强BERT模型的中文命名实体识别 | |
| 引用本文： | 胡婕,胡燕,刘梦赤,张龑. 基于知识库实体增强BERT模型的中文命名实体识别[J]. 计算机应用, 2022, 42(9): 2680-2685. DOI: 10.11772/j.issn.1001-9081.2021071209 |
| 作者姓名： | 胡婕 胡燕 刘梦赤 张龑 |
| 作者单位： | 湖北大学 计算机与信息工程学院，武汉 430062 华南师范大学 计算机学院，广州 510631 |
| 基金项目： | 国家自然科学基金资助项目(61977021);广州市大数据与智能教育重点实验室资助项目(201905010009) |
| 摘 要： | 针对预训练模型BERT存在词汇信息缺乏的问题，在半监督实体增强最小均方差预训练模型的基础上提出了一种基于知识库实体增强BERT模型的中文命名实体识别模型OpenKG+Entity Enhanced BERT+CRF。首先，从中文通用百科知识库CN-DBPedia中下载文档并用Jieba中文分词抽取实体来扩充实体词典；然后，将词典中的实体嵌入到BERT中进行预训练，将训练得到的词向量输入到双向长短期记忆网络（BiLSTM）中提取特征；最后，经过条件随机场（CRF）修正后输出结果。在CLUENER 2020 和 MSRA数据集上进行模型验证，将所提模型分别与Entity Enhanced BERT Pre-training、BERT+BiLSTM、ERNIE和BiLSTM+CRF模型进行对比实验。实验结果表明，该模型的F1值在两个数据集上比四个对比模型分别提高了1.63个百分点和1.1个百分点、3.93个百分点和5.35个百分点、2.42个百分点和4.63个百分点以及6.79个百分点和7.55个百分点。可见，所提模型对命名实体识别的综合效果得到有效提升，F1值均优于对比模型。 |
| 关 键 词： | 命名实体识别 知识库 实体词典 预训练模型 双向长短期记忆网络 |
| 收稿时间： | 2021-07-12 |
| 修稿时间： | 2021-09-18 |
Chinese named entity recognition based on knowledge base entity enhanced BERT model |
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| Jie HU,Yan HU,Mengchi LIU,Yan ZHANG. Chinese named entity recognition based on knowledge base entity enhanced BERT model[J]. Journal of Computer Applications, 2022, 42(9): 2680-2685. DOI: 10.11772/j.issn.1001-9081.2021071209 | |
| Authors: | Jie HU Yan HU Mengchi LIU Yan ZHANG |
| Affiliation: | School of Computer Science and Information Engineering，Hubei University，Wuhan Hubei 430062，China School of Computer Science，South China Normal University，Guangzhou Guangdong 510631，China |
| Abstract: | Aiming at the problem that the pre-training model BERT （Bidirectional Encoder Representation from Transformers） lacks of vocabulary information， a Chinese named entity recognition model called OpenKG + Entity Enhanced BERT + CRF （Conditional Random Field） based on knowledge base entity enhanced BERT model was proposed on the basis of the semi-supervised entity enhanced minimum mean-square error pre-training model. Firstly， documents were downloaded from Chinese general encyclopedia knowledge base CN-DBPedia and entities were extracted by Jieba Chinese text segmentation to expand entity dictionary. Then， the entities in the dictionary were embedded into BERT for pre-training. And the word vectors obtained from the training were input into Bidirectional Long-Short-Term Memory network （BiLSTM） for feature extraction. Finally， the results were corrected by CRF and output. Model validation was performed on datasets CLUENER 2020 and MSRA， and the proposed model was compared with Entity Enhanced BERT pre-training， BERT+BiLSTM， ERNIE and BiLSTM+CRF models. Experimental results show that compared with these four models， the proposed model has the F1 score increased by 1.63 percentage points and 1.1 percentage points， 3.93 percentage points and 5.35 percentage points， 2.42 percentage points and 4.63 percentage points， 6.79 and 7.55 percentage points， respectively in the two datasets. It can be seen that the comprehensive effect of the proposed model on named entity recognition is effectively improved， and the F1 scores of the model are better than those of the comparison models. |
| Keywords: | Named Entity Recognition (NER) knowledge base entity dictionary pre-training model Bidirectional Long Short-Term Memory (BiLSTM) network |
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