Summary of Evaluating the Efficacy Of Ai Techniques in Textual Anonymization: a Comparative Study, by Dimitris Asimopoulos et al.
Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study
by Dimitris Asimopoulos, Ilias Siniosoglou, Vasileios Argyriou, Sotirios K. Goudos, Konstantinos E. Psannis, Nikoleta Karditsioti, Theocharis Saoulidis, Panagiotis Sarigiannidis
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive analysis of text anonymization techniques, focusing on four machine learning models: Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and Transformers. Each model has unique strengths, with LSTM modeling long-term dependencies, CRF capturing word sequence dependencies, ELMo delivering contextual word representations, and Transformers introducing self-attention mechanisms for scalability. The study compares these models’ synergistic potential in addressing text anonymization challenges. Preliminary results show that individual models like CRF, LSTM, and ELMo outperform traditional methods, while the inclusion of Transformers provides a broader perspective on achieving optimal text anonymisation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at ways to protect personal data in digital information. It compares four special computer programs (models) that can help make text anonymous: CRF, LSTM, ELMo, and Transformers. Each program has its own strengths. They can all be used together to make text anonymization better. Early results show that using these models individually can be more effective than traditional methods. The study also looks at how including the Transformers model can improve anonymization. |
Keywords
» Artificial intelligence » Lstm » Machine learning » Self attention
