Abstract:

As an important part of information extraction, entity relation extraction can perform semantic analysis on smaller grained information and provide basic data support for more tasks. The development of relation extraction can be divided into two methods based on traditional machine learning and deep learning. In recent years, the research based on traditional machine learning has mainly focused on the combination of statistic-based and rule-based. The framework of deep learning has achieved abundant research results by introducing distant supervision, few-shot learning, attention mechanism, reinforcement learning and multi-instance multilabel. The development of entity relation extraction is reviewed and each model is analyzed. Combing the latest developments in deep learning methods, the development direction and trend of entity relationship extraction are prospected.

Key words: entity relation extraction, machine learning, distant supervision, graph convolutional network, reinforcement learning, neural network

摘要：

实体关系抽取作为信息抽取任务的重要组成之一，能够对更小粒度的信息进行语义分析，为更多任务提供数据支持。关系抽取发展至今，总体可分为基于传统机器学习和基于深度学习两种方式。基于传统机器学习的关系抽取研究主要以统计和基于规则相结合的方法为主。基于深度学习的框架通过引入远程监督、小样本学习、注意力机制、强化学习、多示例多标记学习等方法取得了丰富的研究成果。回顾实体关系抽取的发展历程，对每种模型进行分析和讨论；结合深度学习方法的最新动态，对实体关系抽取未来的研究方向和趋势进行展望。

关键词: 实体关系抽取, 机器学习, 远程监督, 图卷积网络, 强化学习, 神经网络