This model has previously been shown to provide excellent performance on multiple tasks , including prediction of association norms , word substitution errors , semantic inferences , and word similarity ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) .
integrating experiential and distributional data to learn semantic representations
Mark Andrews Gabriella Vigliocco David Vinson
Andrews et al. ( 2009 ) and is based on a generalization of Latent Dirichlet Allocation . This model has previously been shown to provide excellent performance on multiple tasks , including prediction of association norms , word substitution errors , semantic inferences , and word similarity ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) . While prior work has used the model only with feature norms and visual attributes , we show that low-level image features are directly compatible with the model and provide improved representations of word meaning .
This model has previously been shown to provide excellent performance on multiple tasks , including prediction of association norms , word substitution errors , semantic inferences , and word similarity ( @@CITATION ; Silberer and Lapata , 2012 ) .