Automated Extraction of Enterprise Litigation Information: A Study Based on the ALBERT-BiLSTM-Attention-CRF Model

This manuscript illuminates the pressing demand of commercial banks for the extraction of enterprise litigation data in risk management. An entity recognition algorithm model rooted in ALBERT-BiLSTM-Attention-CRF has been delineated to actualize the automated extraction of such litigation particulars. By fabricating a litigation data set and juxtaposing it with prevailing models, empirical results signify that our model manifests superior precision and robustness in discerning litigation details. This exploration not only furnishes commercial banks with an efficacious risk-alert mechanism but also augments the realm of natural language processing with respect to entity recognition techniques.