基于混合神经网络的实体关系抽取方法研究

实体关系抽取是信息抽取领域的重要研究内容,对知识库的自动构建起着至关重要的作用。针对非结构化文本实体关系抽取存在上下文环境信息难以准确表征,致使现有抽取模型准确率不能满足实际应用需求的问题,该文提出了一种新型的实体关系抽取模型BiGRU-Att-PCNN。该模型是基于混合神经网络,首先,构建双向门控循环单元(BiGRU)以更好地获取文本序列中的上下文语序的相关信息;然后,采用注意力(Attention)机制来达到自动关注对关系影响力高的序列特征的目的;最后,通过采用分段卷积神经网络(PCNN),从调整后的序列中较好地学习到了相关的环境特征信息来进行关系抽取。该模型在公开的英文数据集SemEval 2010 Task 8上取得了86.71%的F₁值,实验表明,该方法表现出了较好的性能,为信息抽取领域实体关系的自动获取提供了新的方法支持。

Entity Relation Extraction Based on Hybrid Neural Network

Entity relation extraction is an important research issue in the field of information extraction, which plays an important role in the automatic construction of knowledge base. This paper proposes a new entity relationship extraction model based on hybrid neural network, named BiGRU-Att-PCNN. Firstly, a Bi-directional Gated Recurrent Unit (BiGRU) is constructed to better obtain the relevant information of context word order in text sequence. Then, the attention mechanism is used to automatically capture the sequence features with high influence on the relationship. Finally, with Piecewise Convolution Neural Network (PCNN), the relevant environmental feature information is better learned from the adjusted sequence for relation extraction. Experiment on SemEval 2010 Task 8 proves that the proposed method achieves 86.71% in F₁ value.