Text Material Recommendation Method Combining Label Classification and Semantic QueryExpansion

Abstract

In the process of preparing various planning and research reports,researchers often need to collect and read a large amount of text materials according to the proposed catalog or title,not only the workload is large,but the quality cannot be gua-ranteed.To this end,in the field of digital government planning documentation,a text material recommendation method combining label classification and semantic query expansion is proposed.From the perspective of information retrieval,the titles at all levels in the catalog are regarded as query sentences,and the referenced text materials are used as target documents,so as to retrieve and recommend text materials.This method is based on the differential evolution algorithm,organically combining the text material recommendation method based on word vector average,semantic query expansion and label classification,which makes up the shortcoming of the traditional text material recommendation method and achieves to retrieve the text materials with the granularity of paragraphs through the title of catalog.After experimental verification on 10 datasets,the results show that the performance of the proposed method is significantly improved.It can greatly reduce the workload of manual material selection and material classification,as well as reduce the difficulty of documentation.

Keywords

• text material recommendation|information retrieval|digital government|query expansion|differential evolution algorithm