A Joint Extraction Method of Financial Events Based on Multi-Layer Convolutional Neural Networks postprint

Abstract: [Purpose/significance] In order to further improve the effect of event extraction in the financial field, the correlation between the two subtasks of event extraction needs to be enhanced.[Method/process] This paper carried out related research about event extraction on Chinese financial texts,and proposed a joint extraction method of financial events that integrated the pre-training model and a multi-layer convolutional neural network. First, the pre-training model BERT captured the comprehensive semantic information of the sentence sequence, then accessed the multi-layer convolutional architecture designed in this paper——MultiCNN, hierarchically extracted local window and high-dimensional spatial semantic information, realized the two tasks of event recognition and element extraction at the same time, and then introduced contrast loss to further strengthen the association between the two tasks.[Result/conclusion] F1 has reached 82.20% on the Chinese financial event data set, which has a certain improvement over the benchmark extraction models.