document: Leveraging interlingual classification to improve web search In this paper we address the problem of improving accuracy of web search in a smaller , data-limited search market ( search language ) using behavioral data from a larger , data-rich market ( assist language ) . Specifically , we use interlingual classification to infer the search language query 's intent using the assist language click-through data . We use these improved estimates of query intent , along with the query intent based on the search language data , to compute features that encode the similarity between a search result ( URL ) and the query . These features are subsequently fed into the ranking model to improve the relevance ranking of the documents . Our experimental results on German and French languages show the effectiveness of using assist language behavioral data especially , when the search language queries have small click-through data .
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