  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 word sequences, ELMo
delivers contextual word representations using deep bidirectional language
models and Transformers introduce self-attention mechanisms that provide
enhanced scalability. Our study is positioned as a comparative analysis of
these models, emphasising their synergistic potential in addressing text
anonymisation challenges. Preliminary results indicate that CRF, LSTM, and ELMo
individually outperform traditional methods. The inclusion of Transformers,
when compared alongside with the other models, offers a broader perspective on
achieving optimal text anonymisation in contemporary settings.
