Query Classification System Based on Snippet Summary Similarities for NTCIR-10 1CLICK-2 Task

A query classification system for NTCIR-10 1CLICK-2 is described in this paper. The system classifies queries in Japanese and English into eight predefined classes by using support vector machines (SVMs) for classification. Feature vectors are created based on snippet similarities instead of snippet word frequency. These vectors, which have fewer dimensions than those made from raw words, reduce the number of parameters of SVMs. Therefore, the system achieves more generalization and reduces computing resources. Two methods for calculating document similarity, cosine similarity and Jaccard index, were compared. Additionally, two snippet sources, Bing search results given by the task organizer and Yahoo! Japan Web search results, were compared. Other methods that add query string information to snippet information for the feature vectors were compared with the above methods. Our system achieved 0.89 accuracy in the English task by cosine similarity and the Yahoo! Japan Web search results, and 0.86 in the Japanese task by cosine similarity and the Bing search results.