Research On User Intention Recognition And Semantic Matching Methods For Government Question And Answer Services

The construction of government question and answer service is an important part of the Digital transformation of the government,which can help government agencies to provide more high-quality,convenient and efficient services.User intention recognition and semantic intention matching are hot topics in the construction of smart government Questions and answers service(Q&A).Optimizing their performance can not only improve business processing efficiency,but also enhance user satisfaction.In intention recognition tasks,due to the diversity of the types and scope of intentions or needs expressed by users,there are differences in the accuracy of the model’s judgment on different user intention types or intention texts.This may exacerbate the bias of the model towards certain types of intentions,leading to misclassification or neglect of user needs,ultimately reducing the fairness of the model.In semantic intent matching tasks,limited labeled data is needed to train the model due to the scarcity of high-quality labeled data.The scarcity of data makes it difficult to improve the accuracy of user semantic intention matching.Therefore,this paper analyzes user needs from the perspectives of fairness in intention recognition and semantic intention matching,with the aim of building high-quality Q&A services in the field of government affairs.The main content of this paper is as follows:Firstly,this paper designs a two-stage intention recognition method based on threeway decision.In the first stage,the text with insufficient recognition confidence is divided into boundary regions to increase their contribution to the loss of the model;Reduce the contribution of easily recognizable text on non-boundary regions to model loss.Make the model focus on the text on the boundary regions,improving the accuracy of the model on difficult to recognize intent texts.Then,based on the sequential threeway decision,this paper conducts multi granularity identification of user intentions,dynamically adjusts the loss function,and makes the model focus on the types of intentions that are difficult to identify.Through the above two aspects,we can reduce the difference in recognition accuracy of the model on different intention types.Secondly,in order to solve the problem of data scarcity in semantic matching,this paper uses the idea of comparative learning to construct a twin network model and a pseudo label annotation algorithm based on three-way decision to improve matching performance.Based on the idea of comparative learning,a twin network model is constructed to enhance data through the model.Then,in order to improve data quality,this paper combines three-way decision and pseudo labeling technology to reduce the risk of mislabeling in data augmentation.Solve the small sample problem in the field of government affairs through the above two methods.In summary,in order to improve the performance of intention recognition and semantic intention matching in the process of government Q&A services,this paper aims to optimize the quality of government Q&A services by addressing the fairness of the intention recognition model and the small sample problem of semantic intention matching.The method proposed in this paper not only enriches and expands relevant research theories,but also accelerates the construction of a smart government from practical applications.