Studying Topical Relevance with Evidence-based Crowdsourcing

Author:

Inel Oana1,Haralabopoulos Giannis2,Li Dan3,Van Gysel Christophe4,Szlávik Zoltán5,Simperl Elena6,Kanoulas Evangelos3,Aroyo Lora1

Affiliation:

1. Vrije Universiteit Amsterdam, Amsterdam, Netherlands

2. University of Nottingham, Nottingham, England UK

3. University of Amsterdam, Amsterdam, Netherlands

4. Apple Inc., Cupertino, CA, USA

5. IBM CAS Benelux, Amsterdam, Netherlands

6. University of Southampton, Southampton, England UK

Funder

QROWD - Because Big Data Integration is Humanly Possible

IBM PhD Fellowship

STARS4ALL - A Collective Awareness Platform for Promoting Dark Skies in Europe

Google Faculty Research Award

Bloomberg Research Grant

Publisher

ACM

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Randomized Clinical Trials can be Accurately Classified in terms of their Methodological Validity by their Representations and Cochrane Reviewers’ Comments (Preprint);2023-08-21

2. Extending Label Aggregation Models with a Gaussian Process to Denoise Crowdsourcing Labels;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18

3. CrowdGP: a Gaussian Process Model for Inferring Relevance from Crowd Annotations;Proceedings of the Web Conference 2021;2021-04-19

4. Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering;Proceedings of the 29th ACM International Conference on Information & Knowledge Management;2020-10-19

5. s-AWARE: Supervised Measure-Based Methods for Crowd-Assessors Combination;Lecture Notes in Computer Science;2020

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