%0 Journal Article %A CHE Jinli %A TANG Liwei %A DENG Shijie %A SU Xujun %T Distant Supervision Chinese Relation Extraction Based on Dual Attention Mechanism %D 2019 %R 10.3778/j.issn.1002-8331.1806-0438 %J Computer Engineering and Applications %P 107-113 %V 55 %N 20 %X 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. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1806-0438