Cited By
View all- Wang ZLi KYe Y(2023)Improving relation classification effectiveness by alternate distillationApplied Intelligence10.1007/s10489-023-04964-z53:23(28021-28038)Online publication date: 1-Dec-2023
Review of entity relation extraction
Pages 7391 - 7405
Abstract
In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies.
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View all- Wang ZLi KYe Y(2023)Improving relation classification effectiveness by alternate distillationApplied Intelligence10.1007/s10489-023-04964-z53:23(28021-28038)Online publication date: 1-Dec-2023