关键词: 大语言模型, 实体关系抽取, 命名实体识别

Abstract: Entity relation extraction aims to identify entity pairs and their relationships from unstructured text, serving as the foundation for many downstream tasks in natural language processing. With the development of big data and deep learning technologies, significant progress has been made in entity relation extraction research. In recent years, applying large language models to this task has become a new research trend. Large language models, with their ability to automatically extract features and strong generalization capabilities, can significantly enhance the performance of the task. This paper provides a comprehensive review of entity relation extraction methods, categorizing them into two main types based on the evolution of techniques and models. First, the definitions of named entity recognition and relation extraction tasks are introduced. Next, a systematic review of the development of entity relation extraction methods is presented, with an in-depth analysis of the advantages and disadvantages of the corresponding models. Building on this, the paper focuses on the unique advantages of large language model-based methods in addressing entity relation extraction tasks. Furthermore, the characteristics of current mainstream datasets are summarized, along with common evaluation metrics for entity relation extraction, such as precision, recall, and F1 score. Finally, the challenges in current research are analyzed, and future research directions are discussed.

Key words: large language models, entity relationship extraction, named entity recognition