Advancements and Challenges in Named Entity Recognition: A Review of Techniques and Domain Specific Applications

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP),responsible for identifying and categorizing entities such as persons, organizations andlocations from unstructured text. While traditional rule-based and statistical models likeConditional Random Fields (CRF) offered high precision in constrained settings theirscalability and cross-domain adaptability remain limited. Recent breakthroughs in deeplearning, particularly transformer-based architectures like BERT, RoBERTa and GPT,have revolutionized Named Entity Recognition (NER) by offering superior contextual understanding and cross-domain adaptability. This review critically analyzes state-of-the-artNER methodologies, evaluating their effectiveness across key sectors such as healthcare,finance, legal analysis and cybersecurity while identifying challenges and future researchdirections. It highlights key challenges such as entity ambiguity, domain adaptation andresource efficiency while also identifying emerging solutions like few-shot and zero shotlearning. Additionally, the paper presents a comparative analysis of model performance,offering valuable insights into the trade-offs between accuracy, computational efficiencyand domain adaptability. By consolidating recent research, this study outlines futuredirections for developing more robust, scalable and interpretable NER systems. It aims toguide researchers and practitioners in building domain-specific NER applications andadvancing the field toward greater generalization and practical utility.