Abstract :
The aim of this research is twofold. On the one hand,
high accuracy retrieval has been a concern of the information
retrieval community for some time. We aim to
investigate this issue via data fusion. On the other hand,
the correlation among component results has been
proven harmful to data fusion, but it has not been taken
into account in data fusion algorithms. In the hope of
achieving better performance, we propose a group of
algorithms to eliminate the effect of uneven correlation
among component results by assigning different
weights to all component results or their combinations.
Then the linear combination method or a variation is
used for fusion. Extensive experimentation is carried out
to evaluate the performances of these algorithms with
six groups of component results, which are the top 10
systems submitted to Text REtrieval Conference (TREC)
6, 7, 8, 9, 2001, and 2002. The experimental results show
that all eight data fusion methods involved outperform
the best component system on average. Therefore, we
demonstrate that the data fusion technique in general is
effective with accurate retrieval results. The experimental
results also demonstrate that all six methods presented
in this article are effective for eliminating the
effect of uneven correlation among component results.
All of them outperform CombSum and five of them
outperform CombMNZ on average.
