Extracting commodity news events based on self-attention mechanism and average pooling-based graph convolutional network
Commodity News event extraction involves analyzing unstructured sentences in news items to extract the information contained in them.Extracting information from news events on commodities can provide the basis for forecasting supply and demand,predicting prices,and developing question-answering systems.The existing researches generally have the problems that the correction between candidate trigger words and entity vector is not strong and the accuracy of parameter role extraction is not enough.In this study,we propose a model to extract commodity news events(SAT-GCN-DPT)based on a self-attention mechanism,the average pooling-based graph convolutional network,and a dependency parse tree.The model is mainly divided into three modules:a ComBERT pre-training module,a module to classify trigger words based on the self-attention mechanism,and a module to classify parameter roles by using the average pooling-based graph convolution network and dependency parsing tree.The model uses the self-attention mechanism to manipulate the input data and enhance the association between the trigger words,while the results of graph convolution are aggregated by using the average pooling function to restore the association between events and improve the accuracy of classification.The results of experiments on the CON dataset showed that that the proposed model achieved high values of accuracy and the Fl score on tasks of classifying the trigger words and parameter roles.
万方数据
维普
