A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT
In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support....
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1060 |
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| author | Yingjie Xu Xiaobo Tan Xin Tong Wenbo Zhang |
| author_facet | Yingjie Xu Xiaobo Tan Xin Tong Wenbo Zhang |
| author_sort | Yingjie Xu |
| collection | DOAJ |
| description | In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies. |
| first_indexed | 2024-03-08T04:00:17Z |
| format | Article |
| id | doaj.art-248ca464da654ebf93c1f05b796553ce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:00:17Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-248ca464da654ebf93c1f05b796553ce2024-02-09T15:07:34ZengMDPI AGApplied Sciences2076-34172024-01-01143106010.3390/app14031060A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERTYingjie Xu0Xiaobo Tan1Xin Tong2Wenbo Zhang3School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, ChinaIn the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.https://www.mdpi.com/2076-3417/14/3/1060cybersecurity knowledge graphChinese named entity recognitionCSBERTfew-shot datacybersecurity dataset |
| spellingShingle | Yingjie Xu Xiaobo Tan Xin Tong Wenbo Zhang A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT Applied Sciences cybersecurity knowledge graph Chinese named entity recognition CSBERT few-shot data cybersecurity dataset |
| title | A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT |
| title_full | A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT |
| title_fullStr | A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT |
| title_full_unstemmed | A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT |
| title_short | A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT |
| title_sort | robust chinese named entity recognition method based on integrating dual layer features and csbert |
| topic | cybersecurity knowledge graph Chinese named entity recognition CSBERT few-shot data cybersecurity dataset |
| url | https://www.mdpi.com/2076-3417/14/3/1060 |
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