Interface Design for Crowdsourcing Hierarchical Multi-Label Text Annotations

Author:

Stureborg Rickard1ORCID,Dhingra Bhuwan1ORCID,Yang Jun1ORCID

Affiliation:

1. Computer Science, Duke University, United States

Funder

National Science Foundation

Google

Publisher

ACM

Reference62 articles.

1. Alberto Alemanno . 2018. How to counter fake news? A taxonomy of anti-fake news approaches. European journal of risk regulation 9, 1 ( 2018 ), 1–5. Alberto Alemanno. 2018. How to counter fake news? A taxonomy of anti-fake news approaches. European journal of risk regulation 9, 1 (2018), 1–5.

2. Fatma Arslan , Josue Caraballo , Damian Jimenez , and Chengkai Li . 2020 . Modeling Factual Claims with Semantic Frames . In Proceedings of the Twelfth Language Resources and Evaluation Conference. European Language Resources Association , Marseille, France, 2511–2520. https://aclanthology.org/ 2020.lrec-1.306 Fatma Arslan, Josue Caraballo, Damian Jimenez, and Chengkai Li. 2020. Modeling Factual Claims with Semantic Frames. In Proceedings of the Twelfth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 2511–2520. https://aclanthology.org/2020.lrec-1.306

3. A Benchmark Dataset of Check-Worthy Factual Claims

4. Natã  M. Barbosa and Monchu Chen. 2019. Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning . In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems(CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300773 10.1145/3290605.3300773 Natã M. Barbosa and Monchu Chen. 2019. Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems(CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300773

5. Samuel R. Bowman Gabor Angeli Christopher Potts and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. https://doi.org/10.48550/arXiv.1508.05326 arXiv:1508.05326 [cs]. 10.48550/arXiv.1508.05326

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