基于动态-分层-对抗协同优化的知识增强BERT文本分类模型OA

Pre-trained language models like BERT excel at capturing general linguistic patterns but often un-derperform in domain-specific text classification due to a lack of structured knowledge.To enhance reasoning capabilities in specialized domains,knowledge injection has emerged as a mainstream approach.However,excessive knowledge fusion may distort the original semantics of sentences,leading to Knowledge Noise(KN).To address domain-specific knowledge gaps and mitigate the impact of KN in text classification,we propose a triple-enhanced BERT framework that integrates a Knowledge-Enhanced Dynamic Attention(KEDA),a Hierarchical Knowledge Fusion Network(HKFN),and an Adversarial Knowledge Regularizer(AKR).Experimental results on seven diverse domain-specific corpora demonstrate that our model signifi-cantly outperforms existing baselines.