May 14, 2024, 4:49 a.m. | Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou, Sotirios K. Goudos, Konstantinos E. Psannis, Nikoleta Karditsioti, Theocharis Saoulidis,

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.06709v1 Announce Type: new
Abstract: In the digital era, with escalating privacy concerns, it's imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information. This research embarks on a comprehensive examination of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture. Each model presents unique strengths since LSTM is modeling long-term dependencies, CRF captures dependencies among …

abstract ai techniques anonymization arxiv comparative study concerns cs.ai cs.cl data digital fields information intrinsic privacy private data protect random research robust strategies study text textual type value while

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