Graph-Based Web Query Classification

Abstract

Understanding Web users' search intent expressed by their queries is essential for a search engine to provide the appropriate answers. Web query classification (QC) algorithms have been widely studied to improve the accuracy and meet users' demands. Some QC algorithms convert queries into vectors and use SVM or CRF model as the classifier. However, with the volume of data increasing, the time consumed significantly increases. In this paper, we propose a method in which we split the queries into words and convert queries into a graph, after that, we adopt a liner equation as the classifier. Experimental results exhibit that our method has similar accuracy but higher efficiency compared with the existing methods. Our method can decrease the training time by 10% compared with the SVM algorithm, and also outperform the CRF model.