Project details for Multi Annotator Supervised LDA for classification

Logo Multi Annotator Supervised LDA for classification 1.0

by fmpr - January 16, 2017, 18:01:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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MA-sLDAc is a C++ implementation of the supervised topic models with labels provided by multiple annotators with different levels of expertise, as proposed in:

  • Rodrigues, F., Lourenço, M, Ribeiro, B, Pereira, F. Learning Supervised Topic Models for Classification and Regression from Crowds. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.

  • Rodrigues, F., Lourenço, M, Ribeiro, B, Pereira, F. Learning supervised topic models from crowds. The Third AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2015.

The code is based on the supervised LDA (sLDA) implementation by Chong Wang and David Blei ( Three different variants of the proposed model are provided:

  • MA-sLDAc (mle): This implementation uses maximum likelihood estimates for the topics distributions (beta) and the annotators confusion matrices (pi);
  • MA-sLDAc (smooth): This implementation places priors on beta and pi and performs approximate Bayesian inference;
  • MA-sLDAc (svi): This implementation is similar to the “MA-sLDAc (smooth)”, but uses stochastic variational inference.

For simplicity reasons, I recommend first-time users to start with "MA-sLDAc (mle)", since this version has less parameters that need to be specified.

Sample multiple-annotator data using the 20newsgroups dataset and more datasets are available here:

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Mac
Data Formats: Various
Tags: Topic Modeling, Supervised Learning, Crowdsourcing
Archive: download here


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