Explainable Named Entity Recognition
Explainable Named Entity Recognition (XAI-NER) kombinerer en standard NER-model — typisk en BERT-baseret eller BiLSTM-CRF sekvenslabeler — med post-hoc eller intrinsiske forklaringsmetoder såsom LIME, SHAP, attention-visualisering eller gradient-baseret saliency for at afdække, hvorfor hvert token blev tildelt en bestemt entitetslabel. Denne gennemsigtighed er essentiel i domæner med høje indsatser såsom kliniske tekster, juridiske dokumenter og biomedicinsk litteratur.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Explainable Named Entity Recognition (XAI-NER). ScholarGate. https://scholargate.app/da/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.
- BERT-baseret klassifikationDyb læring↔ compare
- Forklarbar BERT-baseret klassifikationDyb læring↔ compare
- Forklarlig SentimentanalyseDyb læring↔ compare
- Forklarende TekstresuméDyb læring↔ compare
- Forklarlig TransformerDyb læring↔ compare
- Navngiven enhedsgenkendelse (NER)Tekstmining↔ compare
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