Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
A semisupervised learning method to merge search engine results
ACM Transactions on Information Systems (TOIS)
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic graphical model for joint answer ranking in question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A study of learning a merge model for multilingual information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
CLEF 2005: multilingual retrieval by combining multiple multilingual ranked lists
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Selection and merging strategies for multilingual information retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Ranking multilingual documents using minimal language dependent resources
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Is a query worth translating: ask the users!
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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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 determined collaboratively while the model approaching to its thermal equilibrium. Furthermore, the model parameters are trained under two settings: (1) optimize the accuracy of identifying relevant documents; (2) directly optimize information retrieval evaluation measures, such as mean average precision. Benchmarks show that our model significantly outperforms the existing approaches for MLIR tasks.