We use the same method as Andrews et al. ( 2009 ) for generating our multimodal corpora : for each word token in the text corpus , a feature is selected stochastically from the word 's feature distribution , creating a word-feature pair .
integrating experiential and distributional data to learn semantic representations
Mark Andrews Gabriella Vigliocco David Vinson
In order to evaluate our algorithms , we first need to generate multimodal corpora for each of our nontextual modalities . We use the same method as Andrews et al. ( 2009 ) for generating our multimodal corpora : for each word token in the text corpus , a feature is selected stochastically from the word 's feature distribution , creating a word-feature pair . Words without grounded features are all given the same placeholder feature , also resulting in a wordfeature pair .5 That is , for the feature norm modality , we generate ( word , feature norm ) pairs ; for the SURF modality , we generate ( word , codeword ) pairs , etc. .
We use the same method as @@CITATION for generating our multimodal corpora : for each word token in the text corpus , a feature is selected stochastically from the word 's feature distribution , creating a word-feature pair .