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Table 4 Performance of CombMNZ

From: A robust approach to optimizing multi-source information for enhancing genomics retrieval performance

Components w/ Normalization w/ Assigned Weights w/ Multiple
  document aspect passage2 document aspect passage2 document aspect passage2
Best of baselines 0.2906 0.2189 0.0988 0.2906 0.2189 0.0988 0.2906 0.2189 0.0988
UniNE1+York07ga2+kyoto1 0.2671 (-8.08%) 0.1535 (-29.86%) 0.0937 (-5.13%) 0.2729 (-6.09%) 0.1854 (-15.27%) 0.0957 (-3.19%) 0.2571 (-11.53%) 0.1547 ( -29.33% ) 0.0924 ( -6.49%)
UniNE1+York07ga2+UBexp1 0.2656 (-8.61%) 0.1772 (-19.03%) 0.0879 (-10.99%) 0.2591 (-10.82%) 0.1878 (-14.18%) 0.0867 (-12.30%) 0.2639 (-9.16%) 0.1753 (-19.92%) 0.0885 (-10.43%)
UniNE1+MuMshFd+kyoto1 0.2559 (-11.95%) 0.1801 (-17.70%) 0.0985 (-0.30%) 0.2503 (-13.85%) 0.1837 (-16.09%) 0.0908 (-8.06%) 0.2401 (-17.38%) 0.1599 (-26.96%) 0.0958 (-3.04%)
UniNE1+MuMshFd+UBexp1 0.2416 (-16.85%) 0.1720 (-21.43%) 0.0871 (-11.86%) 0.2466 (-15.11%) 0.1787 (-18.36%) 0.0839 (-15.09%) 0.2419 (-16.74% ) 0.1716 (-21.61% ) 0.0872 (-11.72%)
  1. In order to deeply evaluate the benefits of CombMNZ, we generate CombMNZ-with-normalization, CombMNZ-with-assigned-weight and CombMNZ-with-multiple respectively. For CombMNZ-with-normalization, we employ the standard zero-one normalization method in which all the base weights are scaled between zero being the lowest value and one being the absolute highest value. For CombMNZ-with-assigned-weight, the baselines earn their weights depending on their models. Only the optimal results are presented. For CombMNZ-with-multiple, we apply the CombMNZ method for multiple times (m times, where m is set to be one of {1, 2, 3, 5}). No normalization and no additional weights has been given to the baselines. Only the optimal results are presented as well. The values in the parentheses are the relative rates of improvement over the best results of the baselines. Note that “w/” stands for “with”.
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