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Efficient Test Collection Construction via Active Learning
[article]
2018
arXiv
pre-print
To create a new IR test collection at minimal cost, we must carefully select which documents merit human relevance judgments. Shared task campaigns such as NIST TREC determine this by pooling search results from many participating systems (and often interactive runs as well), thereby identifying the most likely relevant documents in a given collection. While effective, it would be preferable to be able to build a new test collection without needing to run an entire shared task. Toward this end,
arXiv:1801.05605v2
fatcat:ssaz5gvat5h43njyf5difo7vju