Named entity recognition in Vietnamese document using machine learning and application in ensuring cyber security

Authors

  • Nguyễn Ngọc Toàn People's Security Academy
  • Lê Xuân Tuấn
  • Lương Thế Dũng
  • Trần Nghi Phú

DOI:

https://doi.org/10.54654/isj.v1i16.824

Keywords:

named entity recognition, NER system, machine learning, Vietnamese text; negative, reactionary

Tóm tắt

Abstract Named Entity Recognition (NER) in Vietnamese documents is currently a challenging task because of the lacking of standard datasets, or these datasets might be not large enough. Moveover, recognition models are often built mainly based on deep learning methods. In this paper, we present a systematic approach in building entity recognition models of Vietnamese documents, beginning with collecting and building data sets then applying and refining machine learning models. In addition to that, we also propose some scenarios of application which proof the capability of our model in dealing with information security problems. Specifically, we built a dataset of more than 5000 documents collected from social networks using Vietnamese, naming and assigning 1 of 4 predefined labels to the entities in the documents and then apply the pre-training model XLM-RoBERTa with the appropriate fine-tuned initial parameters to recognize these entities. Preliminary results show that the proposed system is effective with the ability to recognize the entity of the model and achieve the F1- measure up to 95.6%, which is better than some NER systems curently available for Vietnamese documents on the same dataset which we have built. The proposed model has been used in building support systems for cybersecurity protection currently.

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References

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Published

2023-02-13

How to Cite

Toàn, N. N., Tuấn, L. X., Dũng, L. T., & Phú, T. N. (2023). Named entity recognition in Vietnamese document using machine learning and application in ensuring cyber security. Journal of Science and Technology on Information Security, 2(16), 39-49. https://doi.org/10.54654/isj.v1i16.824

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Papers