A study of learning a merge model for multilingual information retrieval

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 significantl...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Ming-Feng Tsai, Yu-Ting Wang, Hsin-Hsi Chen
• Keywords: Information Retrieval, Information Technology, Merge Model, Ranking Algorithm, SIGIR 2008