A network framework for noisy label aggregation in social media

Xueying ZHAN; Yaowei WANG; Yanghui RAO; Haoran XIE; Qing LI; Fu Lee WANG; Tak Lam WONG

doi:10.18653/v1/P17-2077

A network framework for noisy label aggregation in social media

Xueying ZHAN, Yaowei WANG, Yanghui RAO, Haoran XIE, Qing LI, Fu Lee WANG, Tak Lam WONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics
Place of Publication	Canada
Publisher	Association for Computational Linguistics
Pages	484-490
Number of pages	7
Volume	2
ISBN (Print)	9781945626760
DOIs	https://doi.org/10.18653/v1/P17-2077
Publication status	Published - Jul 2017
Externally published	Yes
Event	55th Annual Meeting of the Association for Computational Linguistics - Westin Bayshore Hotel, Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017 http://acl2017.org

Publication series

Name	ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume	2

Conference

Conference	55th Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2017
Country/Territory	Canada
City	Vancouver
Period	30/07/17 → 4/08/17
Internet address	http://acl2017.org

Funding

The authors are thankful to the reviewers for their constructive comments and suggestions on this paper. The work described in this paper was supported by the National Natural Science Foundation of China (61502545), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), the Start-Up Research Grant (RG 37/2016-2017R), and the Internal Research Grant (RG 66/2016-2017) of The Education University of Hong Kong.

Access to Document

10.18653/v1/P17-2077

Cite this

ZHAN, X., WANG, Y., RAO, Y., XIE, H., LI, Q., WANG, F. L., & WONG, T. L. (2017). A network framework for noisy label aggregation in social media. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 2, pp. 484-490). (ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers); Vol. 2). Association for Computational Linguistics. https://doi.org/10.18653/v1/P17-2077

ZHAN, Xueying ; WANG, Yaowei ; RAO, Yanghui et al. / A network framework for noisy label aggregation in social media. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vol. 2 Canada : Association for Computational Linguistics, 2017. pp. 484-490 (ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)).

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title = "A network framework for noisy label aggregation in social media",

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{\textquoteright}s topics and the annotators{\textquoteright} 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.",

author = "Xueying ZHAN and Yaowei WANG and Yanghui RAO and Haoran XIE and Qing LI and WANG, {Fu Lee} and WONG, {Tak Lam}",

year = "2017",

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note = "55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 ; Conference date: 30-07-2017 Through 04-08-2017",

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ZHAN, X, WANG, Y, RAO, Y, XIE, H, LI, Q, WANG, FL & WONG, TL 2017, A network framework for noisy label aggregation in social media. in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. vol. 2, ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 2, Association for Computational Linguistics, Canada, pp. 484-490, 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 30/07/17. https://doi.org/10.18653/v1/P17-2077

A network framework for noisy label aggregation in social media. / ZHAN, Xueying; WANG, Yaowei; RAO, Yanghui et al.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vol. 2 Canada: Association for Computational Linguistics, 2017. p. 484-490 (ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers); Vol. 2).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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ZHAN X, WANG Y, RAO Y, XIE H, LI Q, WANG FL et al. A network framework for noisy label aggregation in social media. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vol. 2. Canada: Association for Computational Linguistics. 2017. p. 484-490. (ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)). doi: 10.18653/v1/P17-2077

A network framework for noisy label aggregation in social media

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