Machine learningDeep learning / NLP / CV
可解释命名实体识别
可解释命名实体识别（XAI-NER）将标准的NER模型——通常是基于BERT或BiLSTM-CRF的序列标注器——与事后或内在的可解释性技术（如LIME、SHAP、注意力可视化或基于梯度的显著性）相结合，以揭示每个词元被分配特定实体标签的原因。这种透明度在高风险领域（如临床文本、法律文件和生物医学文献）至关重要。
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来源
- 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. link ↗
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. DOI: 10.1145/2939672.2939778 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Named Entity Recognition (XAI-NER). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-named-entity-recognition
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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