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.
|Title of host publication||Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics|
|Place of Publication||Stroudsburg, PA|
|Publisher||The Association for Computational Linguistics|
|Publication status||Published - 2017|