限定域关系抽取研究综述

随着当前知识图谱的蓬勃发展,关系抽取作为信息抽取的关键一环,已受到越来越多研究者的关注.关系抽取发展至今,总体可以分为基于模板的抽取方法和基于机器学习的抽取方法;之后随着深度学习抽取方法的广泛应用,关系抽取的性能得到了较大提高.本文利用时间顺序法对限定域条件下二元关系抽取方法进行归纳总结.首先对关系抽取的概念定义、数据集以及评价指标等内容进行了简要介绍;随后对关系抽取的相关方法进行了系统梳理,重点分析了目前研究较热的深度学习关系抽取方法;最后对关系抽取的未来研究方向及其应用进行了分析和展望.

Survey on Relation Extraction in Restricted Domain

Amid the vigorous development of the knowledge graph, relation extraction, as a key part of information extraction, has attracted increasing attention from researchers. In general, relation extraction can be divided into template-based extraction and machine learning-based extraction. Later, with the extensive application of the extraction methods based on deep learning, the performance of relation extraction has been greatly improved. In this study, the time sequence method is employed to summarize the extraction methods of binary relations in a restricted domain. This study first briefly introduces the concept, data set, and evaluation indicators of relation extraction. Then it systematically sorts out the related extraction methods and highlights the current research on the relation extraction methods based on deep learning. Finally, it analyzes the future research direction and application of relation extraction.