SOTAVerified

De-identification

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.

Papers

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Showing 81–90 of 174 papers

TitleStatusHype
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks—0
Open video data sharing in developmental and behavioural science—0
Towards Privacy-Preserving Person Re-identification via Person Identify Shift—0
End-to-end speech recognition modeling from de-identified data—0
The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models—0
Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats—0
Spanish Datasets for Sensitive Entity Detection in the Legal Domain—0
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data—0
MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
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