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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 41–50 of 174 papers

Title	Date	Tasks	Status	Hype	Score
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	0	5
DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text	Jul 1, 2018	De-identification	CodeCode Available	0	5
Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs	Jul 8, 2024	De-identificationIn-Context Learning	CodeCode Available	0	5
Hide-and-Seek Privacy Challenge	Jul 23, 2020	De-identificationSynthetic Data Generation	CodeCode Available	0	5
Automated Privacy-Preserving Techniques via Meta-Learning	Jun 24, 2024	De-identificationMeta-Learning	CodeCode Available	0	5
DeIDClinic: A Multi-Layered Framework for De-identification of Clinical Free-text Data	Oct 2, 2024	De-identification	CodeCode Available	0	5
De-identification of Privacy-related Entities in Job Postings	May 24, 2021	De-identificationMulti-Task Learning	CodeCode Available	0	5
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain	May 19, 2020	De-identification	CodeCode Available	0	5
Natural Language Generation for Electronic Health Records	Jun 1, 2018	DecoderDe-identification	CodeCode Available	0	5
De-identification of Patient Notes with Recurrent Neural Networks	Jun 10, 2016	De-identificationFeature Engineering	CodeCode Available	0	5

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