方法对比

	可解释命名实体识别	可解释 Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2018–2020	2017–2021
提出者	Community-driven (NLP + XAI research)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
类型	Interpretability-augmented sequence labeling	Interpretable deep learning model
开创性文献	Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL-IJCNLP), pp. 447–459.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
别名	XAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NER	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
相关	6	4
摘要	Explainable Named Entity Recognition (XAI-NER) combines a standard NER model — typically a BERT-based or BiLSTM-CRF sequence labeler — with post-hoc or intrinsic explainability techniques such as LIME, SHAP, attention visualization, or gradient-based saliency to reveal why each token was assigned a particular entity label. This transparency is essential in high-stakes domains like clinical text, legal documents, and biomedical literature.	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.