Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The impact of database selection on distributed searching
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Cross-language information retrieval: experiments based on CLEF 2000 corpora
Information Processing and Management: an International Journal
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Modeling search engine effectiveness for federated search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A merging strategy proposal: The 2-step retrieval status value method
Information Retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
FRank: a ranking method with fidelity loss
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
Mixture model with multiple centralized retrieval algorithms for result merging in federated search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we present a number of features that may influence the MLIR merging process. These features are mainly extracted from three levels: query, document, and translation. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model. To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method. Moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. The experimental results show that our proposed method can significantly improve merging quality on two different types of datasets. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process. This information might provide us more insight and understanding into MLIR merging.