A Network Framework for Noisy Label Aggregation in Social Media

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

5 Scopus Citations
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Author(s)

  • Yaowei Wang
  • Yanghui Rao
  • Haoran Xie
  • Qing Li
  • Fu Lee Wang
  • Tak-Lam Wong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 55th Annual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics (ACL)
Pages484-490
Volume2
ISBN (Print)9781945626760
Publication statusPublished - Aug 2017

Conference

Title55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
PlaceCanada
CityVancouver
Period30 July - 4 August 2017

Link(s)

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.

Research Area(s)

Citation Format(s)

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 Association for Computational Linguistics (ACL), 2017. p. 484-490.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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