关键词: 关系分类, 选择性注意力, 卷积神经网络

Abstract: Relation classification plays an important part in the structuralization of text data via information extraction. As convolution neural network (CNN) based method can extract high-level features through convolution filters, it achieves competitive performance with other complex-structured networks by only using a standard convolution layer, a pooling layer and a softmax layer. However, when handling samples with long distance between entities, CNN fails to extract effective features, and even extracts the wrong ones from clauses, which results in downgrades of accuracy. Besides, it is observed that existing methods produce inconsistent results when fed with forward and backward instances of the same sample. Therefore, this paper presents a revised CNN based sentence encoder using the shortest dependency paths with selective attention, which only extracts valid sentence features. This paper also introduces the forward instance and backward instance, and incorporates forward and backward instances for relation classification. Experiments verify that the revised relation classification framework and method provide state-of-the-art performance, even without additional artificial features.

Key words: relation classification, selective attention, convolution neural network