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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/64481


    Title: Learning a Merge Model for Multilingual Information Retrieval
    Authors: Tsai, Ming-Feng
    蔡銘峰
    Chen, Hsin-Hsi
    Wang, Yu-Ting
    Contributors: 資科系
    Keywords: Learning to merge;Merge model;MLIR
    Date: 2011.09
    Issue Date: 2014-03-06 16:29:28 (UTC+8)
    Abstract: 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.
    Relation: Information Processing and Management, 47(5), 635-646
    Data Type: article
    DOI link: http://dx.doi.org/10.1016/j.ipm.2009.12.002
    DOI: 10.1016/j.ipm.2009.12.002
    Appears in Collections:[Department of Computer Science ] Periodical Articles

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