基于循环卷积神经网络的实体关系抽取方法研究

针对目前大多数关系抽取中对于文本语料中较长的实体共现句，往往只能获取到局部的特征，并不能学习到长距离依赖信息的问题，提出了一种基于循环卷积神经网络与注意力机制的实体关系抽取模型。将擅长处理远距离依赖关系的循环神经网络GRU加入到卷积神经网络的向量表示阶段，通过双向GRU学习得到词语的上下文信息向量，在卷积神经网络的池化层采取分段最大池化方法，在获取实体对结构信息的同时，提取更细粒度的特征信息，同时在模型中加入基于句子级别的注意力机制。在NYT数据集的实验结果表明提出方法能有效提高实体关系抽取的准确率与召回率。

Relation extraction based on recurrent convolutional neural network

Most of the relation extraction approaches could not learn the long distance dependence information from the long sentences with entity co-occurrence. This paper proposed a new relation extraction model to solve this problem. It was based on the recurrent convolutional neural network and the sentence-level attention mechanism. It used the Bi-GRU neural network to learn context vectors for words. And it adopted the piecewise maximum pooling method, which could obtain fine grained features. Experimental results on the NYT dataset demonstrate that this proposed method outperforms the baseline systems.