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 .
integrating experiential and distributional data to learn semantic representations
Mark Andrews Gabriella Vigliocco David Vinson
Griffiths et al. ( 2007 ) helped pave the path for cognitive-linguistic multimodal research , showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis ( Deerwester et al. , 1990 ) in the prediction of association norms . 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 .
@@CITATION 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 .