Query Focused Variable Centroid Vectors for Passage Re-ranking in Semantic Search
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In this paper, we propose a new approach for passage re-ranking. We show that variable (i.e. non-static) centroid vectors for passages, created based on the given query, significantly improves passage re-ranking results compared to that obtained using static centroid vectors. We also show that the results are comparable to RWMD-Q, an existing (non-centroid based unsupervised) state of the art. The experiments reported are conducted on two different datasets in both neural and co-occurrence based distributional semantics settings.
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