Table 6

Performance of the Fusion Approach on Okapi 2007 and 2006

Components

Okapi 2007

Okapi 2006

document

aspect

passage2

document

aspect


word

0.2108

0.1080

0.0364

0.3140

0.1237

sentence

0.1805

0.0970

0.0350

0.3030

0.1206

paragraph

0.1588

0.0616

0.0333

0.3109

0.1410


reciprocal

0.2219 (5.29%)

0.1237 (14.51%)

0.0478 (31.40%)

0.3168 (1.07%)

0.1449 (12.25%)

CombMNZ-with-normalization

0.1703 (-19.20%)

0.0643 (-40.43%)

0.0270 (-25.92%)

0.2352 (-26.55%)

0.0498 (-61.46%)

CombMNZ-with-assigned-weights

0.1777 (-15.72%)

0.0701 (-35.12%)

0.0273 (-24.88%)

0.2441 (-23.78%)

0.0524 (-59.43%)

CombMNZ-with-multiple

0.1730 (-17.93%)

0.0651 (-39.73%)

0.0277 (-24.01%)

0.2375 (-25.85%)

0.0508 (-60.62%)

CombSUM

0.1818 (-13.76%)

0.0718 (-33.56%)

0.0297 (-18.43%)

0.2559 (-20.10%)

0.0719 (-44.32%)


We examine the proposed robust approach on the single model with Okapi BM25. First of all, the baselines are from three different indices under the same IR model, BM25, instead of those from three kind of IR models. Second, three indices are built on the 2007 and 2006 genomics data sets according to three passage extraction methods [11,12]. Here “word” stands for “word-base”, “sentence” for “sentence-base” and “paragraph” for “paragraph-base”. Third, the Okapi tuning parameters of the selected runs are (k1, b) = (0.5, 1.3). The values in the parentheses are the relative rates of improvement over the best results of the baselines.

Hu et al. BMC Bioinformatics 2011 12(Suppl 5):S6   doi:10.1186/1471-2105-12-S5-S6

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