Research on Named Entity Recognition Algorithm Based on NLP
WangYue Qu, JianLiang Li · 2024
In recent years, with the rapid development of natural language processing technology, Named Entity Recognition (NER), as one of the key tasks in the field of NLP, has attracted wide attention. NER plays an important role in knowledge graph construction, machine translation, question answering system and other downstream applications. This paper aims to investigate named entity recognition algorithms based on deep learning, and propose an improved lattice model method (SLBERT) and a new method based on Machine Reading Comprehension (MRC) (GFMRC) to improve the performance of NER. The experimental results demonstrate that our proposed method is effective and achieves state-of-the-art performance compared to other methods.