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

Abstract

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