MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion
Named entity recognition (NER) is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature ada...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6696064 |
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| _version_ | 1832566393507151872 |
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| author | Xuming Han Feng Zhou Zhiyuan Hao Qiaoming Liu Yong Li Qi Qin |
| author_facet | Xuming Han Feng Zhou Zhiyuan Hao Qiaoming Liu Yong Li Qi Qin |
| author_sort | Xuming Han |
| collection | DOAJ |
| description | Named entity recognition (NER) is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature adaptive fusion Chinese named entity recognition (MAF-CNER) model. The model uses bidirectional long short-term memory (BiLSTM) neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. This method can better integrate the two sets of features, thereby improving the model entity recognition ability. In order to fully test the entity recognition performance of this model, we compared the basic model and other mainstream models on Microsoft Research Asia (MSRA) and “China People’s Daily” dataset from January to June 1998. Experimental results show that this model is better than other models, with F1 values of 97.01% and 96.78%, respectively. |
| format | Article |
| id | doaj-art-26b2cd320e054c3881b295e93baab14d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-26b2cd320e054c3881b295e93baab14d2025-02-03T01:04:13ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66960646696064MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive FusionXuming Han0Feng Zhou1Zhiyuan Hao2Qiaoming Liu3Yong Li4Qi Qin5College of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Management, Jilin University, Changchun 130022, Jilin, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin 150006, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaSchool of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaNamed entity recognition (NER) is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature adaptive fusion Chinese named entity recognition (MAF-CNER) model. The model uses bidirectional long short-term memory (BiLSTM) neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. This method can better integrate the two sets of features, thereby improving the model entity recognition ability. In order to fully test the entity recognition performance of this model, we compared the basic model and other mainstream models on Microsoft Research Asia (MSRA) and “China People’s Daily” dataset from January to June 1998. Experimental results show that this model is better than other models, with F1 values of 97.01% and 96.78%, respectively.http://dx.doi.org/10.1155/2021/6696064 |
| spellingShingle | Xuming Han Feng Zhou Zhiyuan Hao Qiaoming Liu Yong Li Qi Qin MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion Complexity |
| title | MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion |
| title_full | MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion |
| title_fullStr | MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion |
| title_full_unstemmed | MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion |
| title_short | MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion |
| title_sort | maf cner a chinese named entity recognition model based on multifeature adaptive fusion |
| url | http://dx.doi.org/10.1155/2021/6696064 |
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