Fin-BERT-Based Event Extraction Method for Chinese Financial Domain.

Event extraction aims to extract human-interest information from massive amounts of unstructured text. Currently, most existing event extraction methods are based on general corpora and rarely consider domain- specific prior knowledge. Moreover, most methods cannot handle well the case where multiple events exist in the same document, and they perform poorly when faced with a large number of negative examples. To address these issues, this paper proposes a model called Fin-PTPCG based on Fin-BERT (financial bidirectional encoder representation from Transformers) and PTPCG (pseudo-trigger-aware pruned complete graph). This method fully utilizes the expression ability of the Fin-BERT pre- training model and incorporates domain- specific prior knowledge during the encoding stage. In the event detection module, multiple binary classifiers are stacked to ensure that the model can effectively identify the situation of multiple events in a document and screen out negative examples. Combined with the decoding module of the PTPCG model, entities are extracted and connected into a complete graph and pruned by calculating a similarity matrix. The problem of unlabeled triggers is solved by selecting pseudo-triggers. Finally, the event extraction is achieved by the event classifier. This method achieves a 0.7 and 3.7 percentage points improvement in F1 score compared to the baselines on the ChFinAnn and Duee-fin datasets for the event extraction task. [ABSTRACT FROM AUTHOR]