Learning Supervised Topic Models for Classification and Regression from Crowds

Filipe Rodrigues, Mariana Lourenco, Bernardete Ribeiro, Francisco Camara Pereira

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    Abstract

    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.
    Original languageEnglish
    JournalI E E E Transactions on Pattern Analysis and Machine Intelligence
    Volume39
    Issue number12
    Pages (from-to)2409-2422
    ISSN0162-8828
    DOIs
    Publication statusPublished - 2017

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