Inter-Person Relation Classification via AttentionBased Bidirectional Gated Recurrent Unit

Abstract

Relation classification is a fundamental ingredient in various information extraction systems. To extract personal entity relation from Chinese text, a novel deep neural network architecture is proposed this paper, which employs bidirectional Gated Recurrent Unit (Bi-GRU) by adding attention mechanism to capture important semantic information in a sentence without hand-crafted features. Considering the complexity of Chinese text, word representation is obtained as a concatenation of word embeddings and character embeddings. Besides, the relative distances of the current word to the entities are added to the word representation to improve the performance of the relation classification. At last, the experimental results demonstrate the proposed model is more effective than state-of-the-art methods.