Merging Multilingual Information Retrieval Results Based on Prediction of Retrieval Effectiveness
نویسندگان
چکیده
This paper deals with Chinese, English and Japanese multilingual information retrieval (MLIR). Merging problem in distributed MLIR is studied. The prediction of retrieval effectiveness is used to determine the merging weight of each intermediate run. The translation penalty and collection weight are considered to improve merging performance. Several merging strategies are experimented. Experimental results show that the performance of normalized-by-top-k merging with translation penalty and collection weight is similar to that of raw-score merging and better than that of the other merging strategies.
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