BG-SAC: Entity relationship classification model based on Self-Attention supported Capsule Networks - Peng, Dunlu; Zhang, Dongdong; Liu, Cong; Lu, Jing (Applied soft computing, 2020)
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. •A relation classification model is formed by combining self-attention mechanism and capsule network.•The model captures the location information of entities and highlight the entity pair information.•Even on a small annotated dataset, the model performs well in relation classification.•Compared with the comparative models, the proposed model improves the accuracy of classification.