Aiming at the problem of low information utilization rate of sentence dependency tree and poor feature extraction effect in relation extraction task, an Attention-guided Gate perceptual Graph Convolutional Network (Att-Gate-GCN) model was proposed. Firstly, a soft pruning strategy based on the attention mechanism was used to assign weights to the edges in the dependency tree through the attention mechanism, thus mining the effective information in the dependency tree and filtering the useless information at the same time. Secondly, a gate perceptual Graph Convolutional Network (GCN) structure was constructed, thus increasing the feature perception ability through the gating mechanism to obtain more robust relationship features, and combining the local and non-local dependency features in the dependency tree to further extract key information. Finally, the key information was input into the classifier, then the relationship category label was got. Experimental results indicate that, compared with the original graph convolutional network relation extraction model, the proposed model has the F1 score increased by 2.2 percentage points and 3.8 percentage points on SemEval2010-Task8 dataset and KBP37 dataset respectively, which makes full use of effective information, and improves the relation extraction ability of the model.

As the most commonly used method of reducing large-scale datasets, simple random sampling usually causes the loss of some clusters when dealing with unevenly distributed dataset. A density biased sampling algorithm based on grid can solve these defects, but both the efficiency and effect of sampling can be affected by the granularity of grid division. To overcome the shortcoming, a density biased sampling algorithm based on variable grid division was proposed. Every dimension of original dataset was divided according to the corresponding distribution, and the structure of the constructed grid was matched with the distribution of original dataset. The experimental results show that density biased sampling based on variable grid division can achieve higher quality of sample dataset and uses less execution time of sampling compared with the density biased sampling algorithm based on fixed grid division.