Using Query Expansion and Classification for Information Retrieval

With the rapid development of the Internet and great capacity of online documents, information retrieval has become an active research topic. This paper proposes a novel information retrieval algorithm based on query expansion and classification. The algorithm is induced by the observation that very short queries with the traditional information retrieval methods often have low precision, although they can get high recall. Our approach attempts to catch more relevant documents by query expansion and text classification. The results of the experiments show that the algorithm we proposed is more precise and efficient than the traditional query expansion methods.