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Learning to rank from relevance judgments distributions
2022
Journal of the Association for Information Science and Technology
LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available documenttopic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models with relevance judgments distributions (either real or synthetically generated) assigned to documenttopic pairs instead of single-valued relevance
doi:10.1002/asi.24629
fatcat:fawmqx3udzg47dysc3z2qedalq