%0 Journal Article
%A CHEN Shu-dong
%A DU Rong
%A OUYANG Xiao-ye
%A WANG Rong
%A LI Wei
%T Distant Supervision Relation Extraction Method Based on Highway Multi-Kernel Network
%D 
%R 10.13190/j.jbupt.2020-071
%J Journal of Beijing University of Posts and Telecommunications
%P 71-76
%V 43
%N 5
%X As a technology that can quickly generate large amounts of labeled data, the distant supervision is increasingly used in relation extraction. However, there are still problems such as insufficient text feature extraction and noise in the bag. A distant supervision relation extraction method based on highway multi-kernel network is proposed to solve these questions. Firstly, the feature of sentences are deeply extracted by highway network and multi-kernel convolution; and then the intra-bag attention mechanism is used to improve the sentence weight of the correct annotation in bag and reduce the intra-bag noise to obtain the bag‘s embedding. Subsequently, the inter-bag attention mechanism is used to reduce the inter-bag noise for each group of bags with the same relation to obtain the group‘s embedding. Finally, groups are used as training samples to train the classifier to achieve relation extraction. Experiment shows that this method has better relation extraction performance than existing methods.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2020-071