Chinese event extraction method based on ABBSAC model

Event extraction, as an important part of information extraction, is the main way to transform unstructured text into valuable structured text. To address the problems of long training time and large model volume commonly found in current event extraction models, the paper proposes chinese event extraction model based on ABBSAC model. Reducing model size with ALBERT pre-trained models, using BiSRU++ to capture the internal association information of the text, and incorporating the attention mechanism to improve the model accuracy, and finally using the output of CRF as the extraction result. Based on Sina news, a corpus is constructed independently and a comparative experiment is carried out. The model achieves higher precision, recall and F1-score with an increase in training speed of about 10% and a cut in the number of model parameters of about 82%, demonstrating the advancedness of the proposed model. Also on the ACE05 and DUEE benchmark datasets, the F1-score for trigger extraction are improved by 1.7% and 0.3%, respectively, and the F1-score for argument role extraction are improved by 5.4% and 0.1%, when compared with the frontier method, effectively improving the effectiveness of the chinese event extraction task.