关键词: 中文关系抽取, 远程监督, 双重注意力机制, 双向门限循环单元（BI-GRU）, 互动百科

Abstract: Compared with the traditional supervised Chinese relation extraction, the method based on distant supervision can greatly avoid the shortage of training corpus, so it has received extensive attention. However, the performance of the methods based on distant supervision is seriously constrained by the wrong labels introduced in the process of constructing corpus. Therefore, in order to alleviate the impact of noisy data, a relation extraction model based on dual attention mechanism is proposed in this paper. The model can obtain the context semantic information of training instances by bidirectional gated recurrent unit network, and focus on the important semantic features in the instances through the character-level attention mechanism. At the same time, the instance-level attention mechanism is introduced to calculate the correlation between instance and the corresponding relation in multiple instances in order to reduce the weight of noisy data. The experimental results on the Chinese character relationship corpus based on hudong encyclopedia show that the model compared to the single attention mechanism  models can effectively utilize the semantic information contained in the instances and reduce the influence of the wrong label instance, and get higher accuracy.

Key words: Chinese relation extraction, distant supervision, dual attention mechanism, Bidirectional Gated Recurrent Unit（BI-GRU）, hudong encyclopedia