Joint Ranking for Multilingual Web Search

Ranking for multilingual information retrieval (MLIR) is a task to rank documents of different languages solely based on their relevancy to the query regardless of query’s language. Existing approaches are focused on combining relevance scores of different retrieval settings, but do not learn the ranking function directly. We approach Web MLIR ranking within the learning-to-rank (L2R) framework. Besides adopting popular L2R algorithms to MLIR, a joint ranking model is created to exploit the correlations among documents, and induce the joint relevance probability for all the documents. Using this method, the relevant documents of one language can be leveraged to improve the relevance estimation for documents of different languages. A probabilistic graphical model is trained for the joint relevance estimation. Especially, a hidden layer of nodes is introduced to represent the salient topics among the retrieved documents, and the ranks of the relevant documents and topics are determi...

• Type: Conference
• Year: 2009
• Where: ECIR
• Authors: Wei Gao, Cheng Niu, Ming Zhou, Kam-Fai Wong
• Keywords: Computer Science, ECIR 2009, Joint Ranking Model, Joint Relevance Estimation, Relevant Documents