In a similar vein , Steyvers ( 2010 ) showed that a different feature-topic model improved predictions on a fill-in-the-blank task .
combining feature norms and text data with topic models
Mark Steyvers
Andrews et al. ( 2009 ) furthered this work by showing that a bimodal topic model , consisting of both text and feature norms , outperformed models using only one modality on the prediction of association norms , word substitution errors , and semantic interference tasks . In a similar vein , Steyvers ( 2010 ) showed that a different feature-topic model improved predictions on a fill-in-the-blank task . Johns and Jones ( 2012 ) take an entirely different approach by showing that one can successfully infer held out feature norms from weighted mixtures based on textual similarity .
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