This article provides an in-depth exploration into Named Entity Recognition (NER) methods, from traditional rule-based strategies to modern transformer architectures like BERT, LSTM, and CNN. The study emphasizes domain-specific NER models, particularly in the fields of finance, legal, and healthcare, and discusses their unique adaptability. The research also explores cutting-edge paradigms, including reinforcement learning and Optical Character Recognition (OCR), and their role in enhancing NER capabilities. The paper concludes by highlighting the challenges and future research avenues in NER.
Publication date: 25 Sep 2023
Project Page: https://arxiv.org/abs/2309.14084v1
Paper: https://arxiv.org/pdf/2309.14084
