Multi Annotator Supervised LDA for classificationhttp://mloss.orgUpdates and additions to Multi Annotator Supervised LDA for classificationenMon, 16 Jan 2017 18:01:36 -0000Multi Annotator Supervised LDA for classification 1.0<html><p>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: </p> <ul> <li><p>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. </p> </li> <li><p>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. </p> </li> </ul> <p>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: </p> <ul> <li> MA-sLDAc (mle): This implementation uses maximum likelihood estimates for the topics distributions (beta) and the annotators confusion matrices (pi); </li> <li> MA-sLDAc (smooth): This implementation places priors on beta and pi and performs approximate Bayesian inference; </li> <li> MA-sLDAc (svi): This implementation is similar to the “MA-sLDAc (smooth)”, but uses stochastic variational inference. </li> </ul> <p>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. </p> <p>Sample multiple-annotator data using the 20newsgroups dataset and more datasets are available here: </p></html>filipe rodriguesMon, 16 Jan 2017 18:01:36 -0000 modelingsupervised learningcrowdsourcing