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 proceedingChapter

2 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. Copyright © 2017 Association for Computational Linguistics.
Original languageEnglish
Title of host publicationProceedings of the 55th Annual Meeting of the Association for Computational Linguistics
Place of PublicationStroudsburg, PA
PublisherThe Association for Computational Linguistics
Pages484-490
Volume2
ISBN (Print)9781945626753
DOIs
Publication statusPublished - 2017

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Labels
Agglomeration
Backpropagation algorithms
Metadata
Costs

Citation

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 (Volume 2: Short Papers) (pp. 484-490). doi: 10.18653/v1/P17-2077