SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction
  Tasks Oriented to the Finance Field
Congqing He Xiangyu Zhu Yuquan Le Yuzhong Liu Jianhong Yin
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.