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An extended study of content and crowdsourcing-related performance factors in named entity annotation.

, , , , and . Semantic Web, 9 (3): 355-379 (2018)

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A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric., , , and . CoRR, (2020)TEM: High Utility Metric Differential Privacy on Text., , , and . SDM, page 883-890. SIAM, (2023)Improving Paid Microtasks through Gamification and Adaptive Furtherance Incentives., , , and . WWW, page 333-343. ACM, (2015)Calibrating Mechanisms for Privacy Preserving Text Analysis., , , and . PrivateNLP@WSDM, volume 2573 of CEUR Workshop Proceedings, page 8-11. CEUR-WS.org, (2020)Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations., , , and . CoRR, (2019)Differentially Private Adversarial Robustness Through Randomized Perturbations., , , , and . CoRR, (2020)Beyond Monetary Incentives: Experiments in Paid Microtask Contests., and . ACM Trans. Soc. Comput., 2 (2): 6:1-6:31 (2019)Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text., , and . ICDM, page 210-219. IEEE, (2019)Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise., , , , and . FLAIRS, (2021)Beyond monetary incentives: experiments in paid microtask contests modelled as continuous-time markov chains., and . CoRR, (2019)