Summary of Sebertnets: Sequence Enhanced Bert Networks For Event Entity Extraction Tasks Oriented to the Finance Field, by Congqing He et al.
SEBERTNets: Sequence Enhanced BERT Networks for Event Entity Extraction Tasks Oriented to the Finance Field
by Congqing He, Xiangyu Zhu, Yuquan Le, Yuzhong Liu, Jianhong Yin
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes two novel models for event entity extraction in the finance field. The Sequence Enhanced BERT Networks (SEBERTNets) leverage the strengths of BERT while capturing sequence semantic information. Additionally, the Hybrid Sequence Enhanced BERT Networks (HSEBERTNets) employ a multi-channel recall method to effectively recall all corresponding event entities. Experimental results demonstrate the effectiveness of these methods, with SEBERTNets achieving an F1 score of 0.905 and HSEBERTNets achieving an F1 score of 0.934 in the first stage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding important events in financial data. It proposes two new models to help us do this better. One model, called SEBERTNets, uses a special type of AI called BERT to understand the meaning of words and phrases. The other model, HSEBERTNets, combines this with a way to find all related events. The results show that these methods work well, which is important for people who invest money or manage assets. |
Keywords
* Artificial intelligence * Bert * F1 score * Recall
