To date, deep learning techniques, especially the combination of convolutional neural networks and recurrent neural networks with the attention mechanism, have been the state-of-the-art solutions for processing relation extraction and classification tasks. However, the neural network model constructed by this method cannot make full use of the labeled entities and their positional information in the relation classification, or even performs poorly on the small sample dataset. To address these issues, this paper proposes an entity relationship classification model BG-SAC, which combines BiGRU, Self-Attention mechanism and Capsule Networks. BG-SAC primarily uses BiGRU to obtain sentence sequential information and context-based semantic information, and then is coupled with the Self-Attention mechanism to get the correlation between words. Capsule Networks are used to acquire the positional information of entities. Eventually the probability that entities belong to a certain relationship category is calculated through the length of a capsule, so as to determine the relationship between entities and realize the classification of entity relationship. The experimental results show that the proposed model can effectively capture the word positional information and improve the classification effect with small sample datasets.

迄今为止，深度学习技术，特别是卷积神经网络和递归神经网络与注意力机制的结合，已经成为处理关系提取和分类任务的最新解决方案。但是，用这种方法构造的神经网络模型不能在关系分类中充分利用标记的实体及其位置信息，甚至在小样本数据集上的表现也不佳。为了解决这些问题，本文提出了一种实体关系分类模型BG-SAC，该模型结合了BiGRU，自我注意机制和胶囊网络。BG-SAC主要使用BiGRU获取句子顺序信息和基于上下文的语义信息，然后结合自注意机制获得单词之间的相关性。胶囊网络用于获取实体的位置信息。最终，通过囊的长度来计算实体属于某个关系类别的概率，从而确定实体之间的关系并实现实体关系的分类。实验结果表明，该模型可以有效地捕获单词位置信息，并利用较小的样本数据集提高分类效果。