Abstract: We participated in two tasks: Multi-8 two-years-on retrieval and Multi-8 results merging. For our multi-8 two-years-on retrieval work, simple multilingual ranked lists are first built by merging ranked lists of different languages that are generated by single types of retrieval algorithms. Then, algorithms are proposed to combine these simple multilingual ranked lists into a single ranked list. Empirical study shows that the approach of combining multilingual retrieval results can substantially improve the accuracies over single multilingual ranked lists. Multi-8 results merging task is our primary interest. This task is viewed as similar to the results merging task of federated search. Query-specific and language-specific models are proposed to calculate comparable document scores for a small amount of documents and estimate logistic models by using information of these documents. The logistic models are used to estimate comparable scores for all documents and thus the documents can be sorted into a final ranked list. A set of experiments demonstrated the advantage of the query-specific and language-specific models against several other alternatives.

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