Summary of A Text Classification Model Combining Adversarial Training with Pre-trained Language Model and Neural Networks: a Case Study on Telecom Fraud Incident Texts, by Liu Zhuoxian et al.
A Text Classification Model Combining Adversarial Training with Pre-trained Language Model and neural networks: A Case Study on Telecom Fraud Incident Texts
by Liu Zhuoxian, Shi Tuo, Hu Xiaofeng
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel text classification model is proposed to efficiently categorize telecom fraud cases into 14 subcategories, replacing manual classification methods that are time-consuming and accurate but inefficient. The model combines adversarial training with a Pre-trained Language Model and neural networks to extract language features, contextual syntactic information, and local semantic information. The model achieved an impressive 83.9% classification accuracy when trained on a portion of telecom fraud case data provided by the operational department. This model has been deployed in the operational department, freeing up significant manpower and improving efficiency in combating Telecom Fraud crimes. The universality of this model makes it suitable for further exploration in other application scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to quickly sort telecom fraud cases into 14 categories. They used a special kind of artificial intelligence called a language model, which can understand and analyze text. This helps police officers categorize these cases more efficiently and accurately than before. The new system is very good at getting things right, with an accuracy rate of 83.9%. It’s already being used by the police department to free up people and make their work easier. Who knows, maybe this technology can help solve other kinds of crimes too! |
Keywords
» Artificial intelligence » Classification » Language model » Text classification
