Few-Shot Learning Sensitive Recognition Method Based on Prototypical Network

Guoquan Yuan, Xinjian Zhao, Liu Li (), Song Zhang and Shanming Wei
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Guoquan Yuan: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Xinjian Zhao: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Liu Li: Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Song Zhang: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Shanming Wei: Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Mathematics, 2024, vol. 12, issue 17, 1-14

Abstract: Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853.

Keywords: sensitive data recognition; NER; BiLSTM; CRF; prototypical network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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