Self-selective attention using correlation between instances for distant supervision relation extraction

Abstract

Distant supervision relation extraction methods are widely used to extract relational facts in text. The traditional selective attention model regards instances in the bag as independent of each other, which makes insufficient use of correlation information between instances and supervision information of all correctly labeled instances, affecting the performance of relation extractor. Aiming at this problem, a distant supervision relation extraction method with self-selective attention is proposed. The method uses a layer of convolution and self-attention mechanism to encode instances to learn the better semantic vector representation of instances. The correlation between instances in the bag is used to assign a higher weight to all correctly labeled instances, and the weighted summation of instances in the bag is used to obtain a bag vector representation. Experiments on the NYT dataset show that the method can make full use of the information of all correctly labeled instances in the bag. The method can achieve better results as compared with baselines.

Keywords: Convolution neural network; Distant supervision relation extraction; Self-attention mechanism.