We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.
Another related work by REF embeds words and phrases in a vector space and uses denotational probabilities for textual entailment tasks.
Learning to Predict Denotational Probabilities For Modeling Entailment
EACL
Computer Science