مقایسهٔ روش‌ها

	توضیح‌پذیری تشخیص موجودیت نام‌دار (Explainable Named Entity Recognition)	ترنسفورمر قابل توضیح (Explainable Transformer)
حوزه	یادگیری عمیق	یادگیری عمیق
خانواده	Machine learning	Machine learning
سال پیدایش	2018–2020	2017–2021
پدیدآور	Community-driven (NLP + XAI research)	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
نوع	Interpretability-augmented sequence labeling	Interpretable deep learning model
منبع بنیادین	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.
نام‌های دیگر	XAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NER	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
مرتبط	6	4
خلاصه	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.