방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 설명 가능한 개체명 인식× | 개체명 인식 (NER)× | |
|---|---|---|
| 분야≠ | 딥러닝 | 텍스트 마이닝 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2018–2020 | — |
| 창시자≠ | Community-driven (NLP + XAI research) | — |
| 유형≠ | Interpretability-augmented sequence labeling | NLP sequence-labelling task |
| 원전≠ | 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 ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| 별칭≠ | XAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NER | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| 관련≠ | 6 | 3 |
| 요약≠ | 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. | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. |
| ScholarGate데이터셋 ↗ |
|
|