Domain-limited relation extraction aims to capture essential text information from the text under the premise of prede-fined entity types and relation types,and mostly uses triples composed of head and tail entities and relations as structured infor-mation representation.As one of the important tasks of information extraction,it plays an important role in question answering and information retrieval.Based on its concepts and task paradigms,this paper systematically sorts out the technical methods in domain-limited relation extraction under the background of deep learning.Whether the entity is visible or not,it is divided into re-lation classification and triplet extraction.According to the performance characteristics of the task,the former can be divided into relation classification under supervised conditions,few-shot relation classification,and relation classification under distant supervi-sion.This paper discusses and analyzes the commonly used technical methods and their advantages and disadvantages in the above tasks.Finally,we summarize the development potential and existing challenges of relation extraction technology in low-resource,multimodal and other situations that are closer to the real world.
NETL
NSTL
万方数据
