Understanding the underlying goal behind a user’s Web query has been proved to be helpful to improve the quality of search. This paper focuses on the problem of automatic identification of query types according to the goals. Four novel entropy-based features extracted from anchor data and click-through data are proposed, and a support vector machines (SVM) classifier is used to identify the user’s goal based on these features. Experimental results show that the proposed entropy-based features are more effective than those reported in previous work. By combining multiple features the goals for more than 97% of the queries studied can be correctly identified. Besides these, this paper reaches the following important conclusions: First, anchor-based features are more effective than click-through-based features; Second, the number of sites is more reliable than the number of links; Third, click-distribution-based features are more effective than session-based ones.
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