Journal article · Ahead of Print article · Preprint article
Learning Supervised Topic Models for Classification and Regression from Crowds
The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications.
In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.
Language: | English |
---|---|
Publisher: | IEEE |
Year: | 2017 |
Pages: | 2409-2422 |
ISSN: | 19393539 , 01628828 and 21609292 |
Types: | Journal article , Ahead of Print article and Preprint article |
DOI: | 10.1109/TPAMI.2017.2648786 |
ORCIDs: | Rodrigues, Filipe and Pereira, Francisco Camara |
Analytical models Data models Inference algorithms Labeling Predictive models Stochastic processes Supervised learning Topic models annotation tasks classification crowds crowdsoucing cs.CL cs.CV cs.HC cs.LG document handling documents inference mechanisms learning (artificial intelligence) multiple annotators real-world applications regression analysis regression problems single-annotator assumption stat.ML stochastic processes stochastic variational inference algorithm supervised learning supervised topic models learning topic modeling