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Msaidizi

Linganisha mbinu

Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.

Utambuzi wa Huluki Zilizotajwa Unaoelezeka×Muhtasari wa Maandishi Unaofafanuliwa×
NyanjaUjifunzaji wa KinaUjifunzaji wa Kina
FamiliaMachine learningMachine learning
Mwaka wa asili2018–20202019–2020
MwanzilishiCommunity-driven (NLP + XAI research)Community (Maynez, Atanasova et al.)
AinaInterpretability-augmented sequence labelingExplainable NLP pipeline
Chanzo asiliaDanilevsky, 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 ↗Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2020). A diagnostic study of explainability techniques for text classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3256–3274. Association for Computational Linguistics. link ↗
Majina mbadalaXAI-NER, Interpretable NER, Transparent Named Entity Recognition, Explainable NERXAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization
Zinazohusiana66
MuhtasariExplainable 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.Explainable Text Summarization augments automatic summarization models — extractive or abstractive — with post-hoc or built-in explanation methods that reveal which source sentences, tokens, or attention patterns drove each output sentence. The goal is to audit faithfulness, detect hallucinations, and build trust in model outputs in high-stakes settings such as medical or legal document review.
ScholarGateSeti ya data
  1. v1
  2. 2 Vyanzo
  3. PUBLISHED
  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

Nenda kwenye utafutaji Pakua slaidi

ScholarGateLinganisha mbinu: Explainable Named Entity Recognition · Explainable Text Summarization. Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/compare