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BERT and the Neural Re-Ranking of Search Results
BERT changed neural search by letting ranking models jointly read a query and candidate passage, using pretrained bidirectional language representations to judge relevance more deeply.
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BERT changed neural search by letting ranking models jointly read a query and candidate passage, using pretrained bidirectional language representations to judge relevance more deeply.
William Pugh’s skip lists replaced rotation-based balancing with randomized levels, offering expected logarithmic search and update performance through a structure that behaves like a hierarchy of linked-list express lanes.