IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i12p2598-d1165216.html
   My bibliography  Save this article

Research on Relation Classification Tasks Based on Cybersecurity Text

Author

Listed:
  • Ze Shi

    (School of Cyber Science and Technology, Beihang University, Beijing 100191, China)

  • Hongyi Li

    (School of Cyber Science and Technology, Beihang University, Beijing 100191, China
    School of Mathematical Sciences, Beihang University, Beijing 100191, China)

  • Di Zhao

    (School of Cyber Science and Technology, Beihang University, Beijing 100191, China
    School of Mathematical Sciences, Beihang University, Beijing 100191, China)

  • Chengwei Pan

    (Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
    Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education, Beijing 100191, China)

Abstract

Relation classification is a significant task within the field of natural language processing. Its objective is to extract and identify relations between two entities in a given text. Within the scope of this paper, we construct an artificial dataset (CS13K) for relation classification in the realm of cybersecurity and propose two models for processing such tasks. For any sentence containing two target entities, we first locate the entities and fine-tune the pre-trained BERT model. Next, we utilize graph attention networks to iteratively update word nodes and relation nodes. A new relation classification model is constructed by concatenating the updated vectors of word nodes and relation nodes. Our proposed model achieved exceptional performance on the SemEval-2010 task 8 dataset, surpassing previous approaches with a remarkable F1 value of 92.3%. Additionally, we propose the integration of a ranking-based voting mechanism into the existing model. Our best results are an F1 value of 92.5% on the SemEval-2010 task 8 dataset and a value 94.6% on the CS13K dataset. These findings highlight the effectiveness of our proposed models in tackling relation classification tasks.

Suggested Citation

  • Ze Shi & Hongyi Li & Di Zhao & Chengwei Pan, 2023. "Research on Relation Classification Tasks Based on Cybersecurity Text," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2598-:d:1165216
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/12/2598/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/12/2598/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu Wang & Yuan Wang & Zhenwan Peng & Feifan Zhang & Fei Yang, 2023. "A Concise Relation Extraction Method Based on the Fusion of Sequential and Structural Features Using ERNIE," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    2. Haoliang Xiong & Zehao Yan & Hongya Zhao & Zhenhua Huang & Yun Xue, 2022. "Triplet Contrastive Learning for Aspect Level Sentiment Classification," Mathematics, MDPI, vol. 10(21), pages 1-14, November.
    3. Zhen Peng & Hongyi Li & Di Zhao & Chengwei Pan, 2023. "Reducing the Dimensionality of SPD Matrices with Neural Networks in BCI," Mathematics, MDPI, vol. 11(7), pages 1-18, March.
    4. Li, Hongyi & Qin, Xuyao & Zhao, Di & Chen, Jiaxin & Wang, Pidong, 2018. "An improved empirical mode decomposition method based on the cubic trigonometric B-spline interpolation algorithm," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 406-419.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jae Hyun Yoon & Jong Won Jung & Seok Bong Yoo, 2024. "Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
    2. Li, Hongyi & Wang, Chaojie & Zhao, Di, 2020. "Preconditioning for PDE-constrained optimization with total variation regularization," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    3. Hu, Gang & Bo, Cuicui & Wei, Guo & Qin, Xinqiang, 2020. "Shape-adjustable generalized Bézier surfaces: Construction and it is geometric continuity conditions," Applied Mathematics and Computation, Elsevier, vol. 378(C).
    4. Weihua Ou & Jianping Gou & Shaoning Zeng & Lan Du, 2023. "Preface to the Special Issue “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics”—Special Issue Book," Mathematics, MDPI, vol. 11(4), pages 1-4, February.
    5. Dehong Zeng & Xiaosong Chen & Zhengxin Song & Yun Xue & Qianhua Cai, 2023. "Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews," Mathematics, MDPI, vol. 11(10), pages 1-16, May.
    6. Jiehai Chen & Zhixun Qiu & Junxi Liu & Yun Xue & Qianhua Cai, 2023. "Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification," Mathematics, MDPI, vol. 11(18), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2598-:d:1165216. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.