Query Expansion Method Based on Generative Model and Pseudo Relevance Feedback in Intelligent Retrieval
[Purpose/Significance]To address the issues of over-reliance on the original retrieved document collection in pseudo-relevance feedback and the neglect of potential expansion elements in relevant documents by generation-augmented retrieval model in retrieval systems,this paper proposes a query expansion model based on generative model and pseudo-relevance feedback.[Method/Process]According to the advantages of both genera-tive models and pseudo relevance feedback,it generated candidate extended word sets using query generative mod-els and pseudo-relevance feedback,respectively.Then,it combined the two sets to obtain the final extended word set,achieving query expansion.Finally,taking NQ and TriviaQA as experimental data,it confirmed the efficiency of the proposed query expansion model using dense passage retrieval.[Result/Conclusion]The experimental results demonstrates that the Top-k retrieval accuracy and EM of the proposed model is higher than the baseline ones.In addition,the effects of the number of pseudo-relevance feedback query words,the context category of the genera-tive model,and the question category on the model performance are tested,and the experimental results verify the effectiveness of the proposed method.The proposed model can improve the quality of query expansion words and information retrieval performance.
万方数据
维普
