A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2010; you can also visit the original URL.
The file type is application/pdf
.
Joint Ranking for Multilingual Web Search
[chapter]
2009
Lecture Notes in Computer Science
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
doi:10.1007/978-3-642-00958-7_13
fatcat:rrr7y4xoh5bezcq63lc2xzftzi