A Study on Entity Extraction for Chinese Financial Statements Incorporating Convolutional Gating and Entity Boundary Prediction
Financial statements play an important role in the development planning of enterprises in the financial field,but extracting effec-tive information from the statements still heavily relies on manual labor.To this end,a named entity recognition method for financial state-ments is proposed that integrates key information and entity boundary information to improve the efficiency of extracting effective information from financial statements.Firstly,a convolutional gating unit consisting of two convolutional layers,self attention mechanism,and gating mechanism is used to extract local features from the encoder's output,screen key information,and guide entity recognition;Then,the entity boundary prediction module is used to integrate the entity boundary information into the long sequence semantic features with sentence depen-dency relationships;Finally,the key information and the long sequence semantic features fused with entity boundary information are input in-to the conditional random field layer to extract the dependencies between adjacent labels that meet the entity labeling rules and obtain the glob-al optimal label sequence.The experiment shows that the F1 values of the proposed model on the Resume and MSRA datasets are 95.75%and 94.92%,respectively,which are better than all comparison models,proving the effectiveness of this method in Chinese named entity recogni-tion;The accuracy,recall,and F1 values on the financial report publication dataset are 87.93%,92.45%,and 90.13%,respectively.Com-pared with the baseline model,the model performs better and can effectively identify named entities in the financial field.
financename entity recognitionconvolutional gating unitsentity boundary predictionconditional random fields
国家科技期刊平台
NSTL
万方数据
