Design And Implementation Of Retrieval System Based On Natural Language Processing

With the rapid expansion and development of the current information age,information resources and data resources are the foundation of everything,and the scope of use of information retrieval is becoming larger and more indispensable.The retrieval system can solve the problems of partial mis-examination,mis-examination and omission in the traditional search method,and cannot provide users with targeted retrieval services.In order to provide a more personalized search service for the company's internal data and internal employees,it can alleviate the inconvenience caused to users by poor search results,reduce the user's search time,improve the user's experience,and design and implement a search system.In the offline segment,the system first crawls from the network,collects text data in the fields of finance and insurance,cleans and annotates,trains to obtain word segmentation models,named entity recognition models and keyword extraction models,and trains word vector models.Then use the previous search record to train the user's intention recognition model.And according to each search result,calculate the relevance score of resources and users.At the same time,the resource quality is scored and sorted according to the content's popularity,quality,evaluation,upload time and other dimensions,and an inverted index table is constructed.The system is on the online side,according to the text input by the user,through the graph neural network model to get the word segmentation results,named entity results,keyword results.The word segmentation results are converted into word vector representations,combined with the named entity results and keyword results,and input into the long-and short-term memory network to obtain the user's intention preference.Finally,preliminary candidate results are obtained based on the named entity results and keyword results,and then the candidate results are reordered according to user intentions and resource relevance scores,the final results are displayed,and the online function of the retrieval system is realized.The retrieval system implements a natural language processing module,an offline scoring module and a resource ranking module,and meets the design requirements in functional testing and performance testing.It can provide a more targeted and personalized retrieval service for the company's internal personnel.Compared with the traditional two-way long-term short-term memory network plus conditional random field model,the graph neural network model has an overall accuracy rate,recall rate and F1 value of 2 to 3 percentage points in word segmentation,named entity recognition and keyword extraction.Promote.The accuracy rate of the results returned by the retrieval system is 90.21%,the recall rate is 89.08%,and the F1 value is 89.64%.