Name
Abstract | ||
|---|---|---|
Large-scale data labeling has become a major bottleneck for many applications, such as machine learning and data integration. This paper presents CrowdGame, a crowdsourcing system that harnesses the crowd to gather data labels in a cost-effective way. CrowdGame focuses on generating high-quality labeling rules to largely reduce the labeling cost while preserving quality. It first generates candidate rules, and then devises a game-based crowdsourcing approach to select rules with high coverage and accuracy. CrowdGame applies the generated rules for effective data labeling. We have implemented CrowdGame and provided a user-friendly interface for users to deploy their labeling applications. We will demonstrate CrowdGame in two representative data labeling scenarios, entity matching and relation extraction.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299869.3320221 | Proceedings of the 2019 International Conference on Management of Data |
Keywords | Field | DocType |
crowdsourcing, data labeling, rule learning | Data integration,Data mining,Bottleneck,Computer science,Crowdsourcing,Data labeling,Relationship extraction | Conference |
ISSN | ISBN | Citations |
0730-8078 | 978-1-4503-5643-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongyu Liu | 1 | 7 | 1.11 |
| Jingru Yang | 2 | 8 | 2.81 |
| Ju Fan | 3 | 406 | 28.58 |
| Zhewei Wei | 4 | 215 | 20.07 |
| Guoliang Li | 5 | 3077 | 154.70 |
| Xiaoyong Du | 6 | 882 | 123.29 |