Event extraction lies at the cores of investment analysis and asset
management in the financial field, and thus has received much attention. The
2019 China conference on knowledge graph and semantic computing (CCKS)
challenge sets up a evaluation competition for event entity extraction task
oriented to the finance field. In this task, we mainly focus on how to extract
the event entity accurately, and recall all the corresponding event entity
effectively. In this paper, we propose a novel model, Sequence Enhanced BERT
Networks (SEBERTNets for short), which can inherit the advantages of the
BERT,and while capturing sequence semantic information. In addition, motivated
by recommendation system, we propose Hybrid Sequence Enhanced BERT Networks
(HSEBERTNets for short), which uses a multi-channel recall method to recall all
the corresponding event entity. The experimental results show that, the F1
score of SEBERTNets is 0.905 in the first stage, and the F1 score of
HSEBERTNets is 0.934 in the first stage, which demonstarate the effectiveness
of our methods.

提出了一种新型模型 SEBERTNets，能够准确提取事件实体和有效识别相应的事件实体，同时捕获序列语义信息；此外，还提出了一种混合序列增强 BERT 模型 HSEBERTNets，采用多通道召回方法来识别所有相应的事件实体。实验结果表明，SEBERTNets 的 F1 分数为 0.905，HSEBERTNets 的 F1 分数为 0.934，证明了方法的有效性。