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: Chapter in Book/Report/Conference proceeding › Conference contribution › 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	ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)
Pages	484-490
Number of pages	7
ISBN (Electronic)	9781945626760
DOIs	https://doi.org/10.18653/v1/P17-2077
Publication status	Published - 2017
Externally published	Yes
Event	55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada Duration: 30 Jul 2017 → 4 Aug 2017

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, ACL 2017
Country/Territory	Canada
City	Vancouver
Period	30/07/17 → 4/08/17

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 ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers) (pp. 484-490). (ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers); Vol. 2). 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. ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). 2017. pp. 484-490 (ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)).

@inproceedings{3caea445d4904dde928e0d39542516a4,

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}",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics.; 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 ; Conference date: 30-07-2017 Through 04-08-2017",

year = "2017",

doi = "10.18653/v1/P17-2077",

language = "English",

series = "ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",

pages = "484--490",

booktitle = "ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)",

}

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 ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 2, pp. 484-490, 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, 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.
ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). 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: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A network framework for noisy label aggregation in social media

AU - Zhan, Xueying

AU - Wang, Yaowei

AU - Rao, Yanghui

AU - Xie, Haoran

AU - Li, Qing

AU - Wang, Fu Lee

AU - Wong, Tak Lam

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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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U2 - 10.18653/v1/P17-2077

DO - 10.18653/v1/P17-2077

M3 - Conference contribution

AN - SCOPUS:85040636152

T3 - ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

SP - 484

EP - 490

BT - ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)

T2 - 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

Y2 - 30 July 2017 through 4 August 2017

ER -

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 ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). 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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