| ¡¡ | Chinese Journal of Computers Full Text |
| Title | Multi-Modal Web Search Query Refinement Based on Semi-Supervised Learning |
| Authors | JIANG Yuan LI Ming ZHOU Zhi-Hua |
| Address | (National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093) |
| Year | 2009 |
| Issue | No.10(2099¡ª2106) |
| Abstract & Background | Abstract Web search systems usually improve search performance by interacting with users to refine queries. In addition to text information, usually a large amount of information of other modalities, such as image, audio and video, exist in Web pages. Few previous researches on Web query refinement, however, try to exploit the multi-modal information. This paper proposes a multi-modal Web search query refinement method M2S2QR based on semi-supervised learning, which transforms Web search query refinement into a machine learning problem. First, based on the information given by Web pages judged by users, classifiers are trained for different modalities, respectively. Then, Web pages that have not been judged by users are used to help improve the performance of the classifiers. Finally the classifiers of different modalities are combined to use. Experiments validate the effectiveness of the proposed method. Keywords machine learning; semi-supervised learning; multi-modal information; Web search; query refinement |