Exploiting bilingual information to improve web search

Wei Gao, John Blitzer, Ming Zhou, Kam-Fai Wong
2009 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - ACL-IJCNLP '09   unpublished
Web search quality can vary widely across languages, even for the same information need. We propose to exploit this variation in quality by learning a ranking function on bilingual queries: queries that appear in query logs for two languages but represent equivalent search interests. For a given bilingual query, along with corresponding monolingual query log and monolingual ranking, we generate a ranking on pairs of documents, one from each language. Then we learn a linear ranking function
more » ... exploits bilingual features on pairs of documents, as well as standard monolingual features. Finally, we show how to reconstruct monolingual ranking from a learned bilingual ranking. Using publicly available Chinese and English query logs, we demonstrate for both languages that our ranking technique exploiting bilingual data leads to significant improvements over a state-of-the-art monolingual ranking algorithm.
doi:10.3115/1690219.1690296 fatcat:se5yimxnrfbyphxijq6ibmwkvy