Relation extraction is a subfield of natural language processing (NLP) that focuses on identifying and extracting relationships between entities mentioned in text. This involves automatically identifying and classifying the semantic relationships that exist between different entities, such as organizations, people, locations, or other entities mentioned in a text. The goal of relation extraction is to extract structured knowledge from unstructured text data, which can be used for various applications such as information retrieval, question answering, summarization, and knowledge graph construction. Researchers in this field use various techniques, such as rule-based systems, machine learning, and deep learning approaches to automatically extract and classify relations between entities in text data. These techniques may involve using linguistic features, dependency parsing, entity recognition, and other NLP methods to improve the accuracy and performance of relation extraction models.