Research Progress of Named Entity Recognition Based on Large Language Model

Abstract

Named entity recognition aims to identify named entities and their types from unstructured text, which is an important basic task in natural language processing technologies such as question answering system, machine translation and knowledge graph. With the development of artificial intelligence, named entity recognition based on large language model has become a hot research topic. This paper reviews the latest research progress of named entity recognition based on large language model. Firstly, the development process of large language model and named entity recognition is summarized, and the commonly used datasets and evaluation methods for named entity recognition tasks are briefly introduced. This paper sorts out the traditional research work on named entity recognition from three aspects: rule-based and dictionary-based, statistical machine learning-based and deep learning-based. Secondly, how to apply different big language models to different fields of named entity recognition tasks is described in detail according to the model architecture, and the existing problems and improvement directions are analyzed. Finally, the challenges faced by named entity recognition tasks based on big language models are summarized, and future research directions are prospected.

Keywords

• large language model; named entity recognition; neural network; deep learning