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Regression Rank: Learning to Meet the Opportunity of Descriptive Queries.

, , and . ECIR, volume 5478 of Lecture Notes in Computer Science, page 90-101. Springer, (2009)

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An improved markov random field model for supporting verbose queries.. SIGIR, page 476-483. ACM, (2009)Linguistic Resources for Speech Parsing., , , , , , , and . LREC, page 629-634. European Language Resources Association (ELRA), (2006)Learning Complementary Policies for Human-AI Teams., , , , , , and . CoRR, (2023)Interpretable by Design: Wrapper Boxes Combine Neural Performance with Faithful Explanations., , and . CoRR, (2023)SQUARE: Benchmarking Crowd Consensus at MediaEval., and . MediaEval, volume 1043 of CEUR Workshop Proceedings, CEUR-WS.org, (2013)Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement?, , , , and . SIGIR, page 805-814. ACM, (2018)Constructing Test Collections using Multi-armed Bandits and Active Learning., , and . WWW, page 3158-3164. ACM, (2019)The Many Benefits of Annotator Rationales for Relevance Judgments., , , and . IJCAI, page 4909-4913. ijcai.org, (2017)Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection., , , and . WWW, page 3689-3700. ACM, (2023)Improving Quality of Crowdsourced Labels via Probabilistic Matrix Factorization., and . HCOMP@AAAI, volume WS-12-08 of AAAI Technical Report, AAAI Press, (2012)