A Study of Learning a Merge Model for Multilingual Information Retrieval
Resource
Proceedings of the 31st Annual International ACM SIGIR Conference, 20-24 July 2008, Singapore, 195-202
Journal
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval-SIGIR 08 SIGIR 08
Pages
195
Date Issued
2008
Date
2008
Author(s)
Abstract
This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we also present a large 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 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 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.
SDGs
Type
conference paper
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