Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo for Advanced Entity Recognition

It is essential to create effective strategies for safeguarding private data while preserving the value of textual data in the face of rising privacy concerns in the digital era. In this research, we give a thorough investigation of Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), and Embeddings from Language Models (ELMo)-based text anonymisation methods [1]. The strengths of CRF, LSTM, and ELMo are combined in the proposed approach to overcome the difficulty of text anonymisation. LSTM is a recurrent neural network architecture that can model long-term dependencies, CRF is a probabilistic graphical model that captures the dependencies among the sequence of words, and ELMo offers contextual word representations utilizing deep bidirectional language models. These models are leveraged in order to identify and subsequently obfuscate sensitive information in the frame of textual anonymisation. Experimental results show that when compared to conventional methods, all three methods—CRF, LSTM, and ELMo—achieve considerable improvements in text anonymisation.