Improved Global Pointer Based Named Entity Recognition Method for Enterprise-benefiting Policies
Small and medium-sized enterprises play an important role in the national economy.In recent years,various preferential policies for enterprises introduced by the government have included key information for government decision-making.However,policy texts have com-plex structures,strong dependence on professional semantics,and contain noisy text and nested entities,making information extraction diffi-cult.Therefore,a named entity recognition model based on multi-level vocabulary global pointers and adversarial training is proposed.This model integrates the LEBERT model at the embedding layer to obtain the combined semantic representation of characters and vocabulary,and constructs a global entity matrix through global pointers to uniformly process flat and nested entities;Simultaneously introducing rotary posi-tion encoding to enhance the perception of position information,and combining it with adversarial training to enhance stability and robustness.The experimental results show that the F1 value of the model is 81.90%,which is 4.72%higher than the classical sequence annotation based model.The overall performance supports downstream task development.
named entity recognitionenterprise-benefiting policiespre-training modelglobal pointeradversarial training
国家科技期刊平台
NSTL
万方数据
