Challenges in named entity recognition

Abstract

This work explores the challenges and advancements in Named Entity Recognition (NER) within natural language processing. The study provides a comprehensive review of the key difficulties faced in NER. Additionally, it examines contemporary methods and models designed to address these challenges, highlighting recent innovations in deep learning and transformer-based architectures. The paper also investigates the impact of linguistic diversity, noisy data, and evolving language on NER performance, offering insights into optimization strategies and future research directions in entity recognition.