Deep Learning and Semantic Concept Spaceare Used in Query Expansion

Abstract

In the practice of information retrieval, there are some problems such as the lack of accurate expression of user query requests, the mismatch between document and query and query optimization. Focusing on these problems, we propose the query expansion method based on conceptual semantic space with deep learning, this hybrid query expansion technique include deep learning and pseudocorrelation feedback, use the deep learning and semantic network WordNet to construct query concept tree in the level of concept semantic space, the pseudo-correlation feedback documents are processed by observation window, compute the co-occurrence weight of the words by using the average mutual information and get the final extended words set. The results of experiment show that the expansion algorithm based on conceptual semantic space with deep learning has better performance than the traditional pseudo-correlation feedback algorithm on query expansion.