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Effect of NEAR n term operation for boosting singles NEAR terms (slop) 1 2 3 4 5 6 7 8 9 10 n 4-gram Recall, Avg. prec. 278 5-gram Recall, Avg. max=915 prec. 270 Ad-Hoc Mono- and Bilingual Retrieval Experiments 39 Table 2. Result overview boosting singles, slop 5 BRF Documents 5 5 5 5 10 10 10 10 4-gram Terms 10 20 30 40 10 20 30 40 Recall, Avg. prec. 233 5-gram Recall, max=915 706 707 711 710 670 675 677 676 Avg. prec. 232 Table 3. Boost values for n-gram-based retrieval experiments # of terms in phrase 1 2 3 4 Boosts according to origin Origin: title Origin: description 3, if short: 10 1, if short: 8 4 2 5 2 5 2 The single most important issue though are short terms.

Since the CLEF 2005 multilingual tasks used only 40 topics of the original 60 topics of the 2003 as the test set (topics 161 to 200), while the first 20 topics (topics 141 to 160) were used as a training set, the average precision of the original 2003 runs was recomputed for the 40 test topics used this year. These revised MAP figures are reported in Table 5. , although the ranking of these runs remains unchanged. It can be seen from Table 5 that the performance difference between the first and the last participant for the 2-Years-On track is much greater (nearly 3 times) than the corresponding difference in 2003, even if the task performed in these two tracks is the same.

4 Result Calculation Evaluation campaigns such as TREC and CLEF are based on the belief that the effectiveness of Information Retrieval Systems (IRSs) can be objectively evaluated by an analysis of a representative set of sample search results. For this, effectiveness measures are calculated based on the results submitted by the participant and the relevance assessments. Popular measures usually adopted for exercises of this type are Recall and Precision. Details on how they are calculated for CLEF are given in [4].

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