A Named Entity Recognition Model Based on BERT Model and Lexical Fusion in the Financial Regulation Field

Based on the needs of financial regulation, in view of the features of high proportion of long entities and rich professional vocabulary information in the text of this field, we proposes model Lexical-Fusion BERT(LF-BERT), which is a named entity recognition (NER) model suitable for this field. LF-BERT obtains character vectors based on BERT, integrates relevant lexical information and inter-lexical correlation information with attention mechanism and bidirectional long and short-term memory network (BiLSTM), and adopts the entity head-end label prediction to realize named entity recognition. Compared with other baseline named entity recognition models, experimental results indicate that LF-BERT obtains the best F1 value on both public and financial regulation field NER datasets, verifying the effectiveness of our method.