Detecting Multilingual and Multi-Regional Query Intent in Web Search


  • Yi Chang Yahoo! Labs
  • Ruiqiang Zhang Yahoo! Labs
  • Srihari Reddy Yahoo! Labs
  • Yan Liu University of Southern California



With rapid growth of commercial search engines, detecting multilingual and multi-regional intent underlying search queries becomes a critical challenge to serve international users with diverse language and region requirements. We introduce a query intent probabilistic model, whose input is the number of clicks on documents from different regions and in different language, while the output of this model is a smoothed probabilistic distribution of multilingual and multi-regional query intent. Based on an editorial test to evaluate the accuracy of the intent classifier, our probabilistic model could improve the accuracy of multilingual intent detection for 15%, and improve multi-regional intent detection for 18%. To improve web search quality, we propose a set of new ranking features to combine multilingual and multi-regional query intent with document language/region attributes, and apply different approaches in integrating intent information to directly affect ranking. The experiments show that the novel features could provide 2.31% NDCG@1 improvement and 1.81% NDCG@5 improvement.




How to Cite

Chang, Y., Zhang, R., Reddy, S., & Liu, Y. (2011). Detecting Multilingual and Multi-Regional Query Intent in Web Search. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1134-1139.