Abstract:In view of the fact that the syntactic relationship is not fully utilized and the argument role is missing in event extraction, an event extraction based on dual attention mechanism (EEDAM) method is proposed to improve the accuracy and recall rate of event extraction. Firstly, sentence coding is based on four embedded vectors and dependency relation is introduced to construct dependency relation graph, so that deep neural network can make full use of syntactic relation. Then, through graph transformation attention network, new dependency arcs and aggregate node information are generated to capture long-range dependencies and potential interactions, weighted attention network is integrated to capture key semantic information in sentences, and sentence level event arguments are extracted to improve the prediction ability of the model. Finally, the key sentence detection and similarity ranking are used to fill in the document level arguments. The experimental results show that the event extraction method based on dual attention mechanism can improve the accuracy rate, recall rate, and F1-score by 17.82%, 4.61%, and 9.80% respectively compared with the optimal baseline joint multiple Chinese event extractor (JMCEE) on ACE2005 data set. On the data set of dam safety operation records, the accuracy, recall rate, and F1 score are 18.08%, 4.41%, and 9.93% higher than the optimal baseline JMCEE, respectively.