Previously LDA has been successfully used to infer unsupervised joint topic distributions over words and feature norms together ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) .
grounded models of semantic representation
Carina Silberer Mirella Lapata
Our experiments are based on the multimodal extension of Latent Dirichlet Allocation developed by Andrews et al. ( 2009 ) . Previously LDA has been successfully used to infer unsupervised joint topic distributions over words and feature norms together ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) . It has also been shown to be useful in joint inference of text with visual attributes obtained using visual classifiers ( Silberer et al. , 2013 ) .
Previously LDA has been successfully used to infer unsupervised joint topic distributions over words and feature norms together ( Andrews et al. , 2009 ; @@CITATION ) .