Joint extraction of agricultural disease entity and relations by combining RoBERTA-WWM and global pointer
Aiming at the problems of polysemy,entity nesting,and triple overlap existing in the process of entity and relation extraction,this paper proposesd a joint extraction model RBGPL that integrates RoBERTa-WWM and Global Pointer network.Firstly,the RoBERTA WWM pre-training model is introduced to overcome the problem of polysemy in different contexts by using context information fusion.Secondly,the global pointer network Global Pointer annotation method was used to solve the problem of entity nesting.Finally,the triple extraction is transformed into the quintuple extraction through the global pointer joint decoding model,which solves the problem of triple overlap.When ran on the self built agricultural disease data set,the accuracy,recall and Fl values of the model RBGPL reached 76.23％,91.18％and 83.04％,which were the best compared with other joint extraction models,and effectively overcame the problem of polysemy and triple overlap.In addition,Fl values of pathogen and crop easily nested entities increased by 3％and 18％,and entity nesting was significantly alleviated.This method improved the performance of Chinese agricultural disease domain entity relationship extraction,and can provide technical support for the construction of agricultural disease domain knowledge map.
万方数据
维普
