Name
Abstract | ||
|---|---|---|
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents. To aggregate noisy labels at a small cost, a network framework is proposed by calculating the matching degree of a document's topics and the annotators' meta-data. Unlike using the back-propagation algorithm, a probabilistic inference approach is adopted to estimate network parameters. Finally, a new simulation method is designed for validating the effectiveness of the proposed framework in aggregating noisy labels. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-2077 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2 |
Field | DocType | Volume |
Internet privacy,World Wide Web,Social media,Computer science,Natural language processing,Artificial intelligence | Conference | P17-2 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueying Zhan | 1 | 8 | 1.12 |
| Yaowei Wang | 2 | 134 | 29.62 |
| Yanghui Rao | 3 | 256 | 23.32 |
| Haoran Xie | 4 | 450 | 71.21 |
| Qing Li | 5 | 3222 | 433.87 |
| Fu Lee Wang | 6 | 926 | 118.55 |
| Tak-lam Wong | 7 | 389 | 35.98 |