A NEW METHOD FOR QUERY EXPANSION BASED ON DEEP LEARNING

This paper tackle the problem of query expansion in Arabic based on ontologies and user data. In this context, a way to extend the search request in order to be able to enrich the request in Arabic is proposed; in order to later integrate it into a system for information retrieval. This system analyzes the search request and then passes it to the ontologies to obtain appropriate concepts, so that the result will be in the form of an expanded request. To ensure results are in line with the requirements and characteristics of each user, personalization based on user data is employed. For evaluation, deep learning models are resorted such us Long short-term memory (LSTM), Gated recurrent neural network (GRU), Bi directionel- Long short-term memory (Bi-LSTM) and Bi-gated recurrent neural network (Bi-GRU). Our results are comparable to best state of the art methods.