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• W4292342405 abstract "This work offers a novel view on the use of human input as labels, acknowledging that humans may err. We build a novel behavioral profile for human annotators which is used as a feature representation of the provided input. We show that by utilizing black-box machine learning, we can take into account human behavior and calibrate their input to improve the labeling quality. To support our claims and provide a proof-of-concept, we experiment with three different matching tasks, namely, schema matching, entity matching and text matching. Our empirical evaluation suggests that the method can improve the quality of gathered labels in multiple settings including cross-domain (across different matching tasks)." @default.
• W4292342405 created "2022-08-19" @default.
• W4292342405 creator A5002899918 @default.
• W4292342405 date "2022-06-12" @default.
• W4292342405 modified "2023-09-24" @default.
• W4292342405 title "HumanAL" @default.
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• W4292342405 doi "https://doi.org/10.1145/3546930.3547496" @default.
• W4292342405 hasPublicationYear "2022" @default.
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