Caizhi Jiang
ABSTRACT
With the widespread popularity of the Internet, offensive text information in cyberspace has attracted widespread attention from society. Currently, offensive text recognition mainly relies on pre constructed sensitive words for recognition, which cannot effectively intercept text without obvious offensive words. This article proposes a dual channel composite model based on the Ernie pre training model. First, the Ernie pre training model is used to construct a dynamic word vector, and more efficient text semantic information is obtained through its internal multi-layer and bidirectional Transformer structure. Then, a dual channel model is added to further refine text information. Bi-GRU is used to extract global semantics and TextCNN is used to extract local information, extracting semantic information features at different abstract levels. The experimental results show that when tested on the Chinese offensive language dataset COLDataset, the accuracy and F1 value of the model are significantly better than the baseline model COLDetector, reaching 83.81% and 82.90%, respectively. This verifies that adding a dual channel fusion network to the Ernie pre trained model can more effectively extract text features and improve the classification performance of the model.
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Index Terms
- Offensive Text Classification based on Ernie??s Dual Channel Composite Model
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