基于BERT和互信息的金融风险逻辑关系量化方法

Quantifying Logical Relations of Financial Risks with BERT and Mutual Information

【目的】通过量化金融风险逻辑关系防控金融风险,同时处理金融事件词频量化不可靠问题。【方法】提出一种结合领域知识的基于BERT和互信息的金融风险逻辑关系量化分析方法,并在通用数据集COPA和金融领域数据集上进行关系量化。【结果】基于BERT和互信息能够有效解决词频量化不可靠问题,该方法在金融风险逻辑关系量化的准确率达到80.1%,较对比方法提升了3.1%~37.4%。【局限】仅考虑了金融领域的语料,在非金融等其他语料上的效果有待检验。【结论】所提方法能够揭示金融风险事件的演化路径,改善金融风险逻辑关系量化的效果。

[Objective]This paper tries to prevent and control financial risks by quantifying their logical relationship,which also improve the reliability of processing word frequency of financial events.[Methods]We proposed a quantitative analysis method for the logical relation of financial risks based on BERT and mutual information combined with domain knowledge.Then,we quantified the relations with COPA and financial data sets.[Results]The proposed model effectively addressed the issue of unreliable quantization of word frequency.Its accuracy reached 80.1%,which was 3.1%~37.4% higher than the benchmark models.[Limitations]More research is needed to examine our new model with non-financial and other corpora.[Conclusions]Our new method can reveal the evolutionary path of financial risk events and improve the effect quantitative presentation of their logical relationship.