Comprehensive Overview of Named Entity Recognition: Models,
  Domain-Specific Applications and Challenges
Kalyani Pakhale
In the domain of Natural Language Processing (NLP), Named Entity Recognition
(NER) stands out as a pivotal mechanism for extracting structured insights from
unstructured text. This manuscript offers an exhaustive exploration into the
evolving landscape of NER methodologies, blending foundational principles with
contemporary AI advancements. Beginning with the rudimentary concepts of NER,
the study spans a spectrum of techniques from traditional rule-based strategies
to the contemporary marvels of transformer architectures, particularly
highlighting integrations such as BERT with LSTM and CNN. The narrative
accentuates domain-specific NER models, tailored for intricate areas like
finance, legal, and healthcare, emphasizing their specialized adaptability.
Additionally, the research delves into cutting-edge paradigms including
reinforcement learning, innovative constructs like E-NER, and the interplay of
Optical Character Recognition (OCR) in augmenting NER capabilities. Grounding
its insights in practical realms, the paper sheds light on the indispensable
role of NER in sectors like finance and biomedicine, addressing the unique
challenges they present. The conclusion outlines open challenges and avenues,
marking this work as a comprehensive guide for those delving into NER research
and applications.