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Event extraction lies at the cores of investment analysis and assetmanagement in the financial field, and thus has received much attention. The2019 China conference on knowledge graph and semantic computing (CCKS)challenge sets up a evaluation competition for event entity extraction taskoriented to the finance field. In this task, we mainly focus on how to extractthe event entity accurately, and recall all the corresponding event entityeffectively. In this paper, we propose a novel model, Sequence Enhanced BERTNetworks (SEBERTNets for short), which can inherit the advantages of theBERT,and while capturing sequence semantic information. In addition, motivatedby recommendation system, we propose Hybrid Sequence Enhanced BERT Networks(HSEBERTNets for short), which uses a multi-channel recall method to recall allthe corresponding event entity. The experimental results show that, the F1score of SEBERTNets is 0.905 in the first stage, and the F1 score ofHSEBERTNets is 0.934 in the first stage, which demonstarate the effectivenessof our methods.

SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction Tasks Oriented to the Finance Field