@inproceedings{zhan-etal-2017-network,
title = "A Network Framework for Noisy Label Aggregation in Social Media",
author = "Zhan, Xueying and
Wang, Yaowei and
Rao, Yanghui and
Xie, Haoran and
Li, Qing and
Wang, Fu Lee and
Wong, Tak-Lam",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2077",
doi = "10.18653/v1/P17-2077",
pages = "484--490",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Network Framework for Noisy Label Aggregation in Social Media
%A Zhan, Xueying
%A Wang, Yaowei
%A Rao, Yanghui
%A Xie, Haoran
%A Li, Qing
%A Wang, Fu Lee
%A Wong, Tak-Lam
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F zhan-etal-2017-network
%X 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.
%R 10.18653/v1/P17-2077
%U https://aclanthology.org/P17-2077
%U https://doi.org/10.18653/v1/P17-2077
%P 484-490
Markdown (Informal)
[A Network Framework for Noisy Label Aggregation in Social Media](https://aclanthology.org/P17-2077) (Zhan et al., ACL 2017)
ACL
- Xueying Zhan, Yaowei Wang, Yanghui Rao, Haoran Xie, Qing Li, Fu Lee Wang, and Tak-Lam Wong. 2017. A Network Framework for Noisy Label Aggregation in Social Media. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 484–490, Vancouver, Canada. Association for Computational Linguistics.