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We test whether distributional models can do one-shot learning ofdefinitional properties from text only. Using Bayesian models, we find thatfirst learning overarching structure in the known data, regularities in textualcontexts and in properties, helps one-shot learning, and that individualcontext items can be highly informative. Our experiments show that our modelcan learn properties from a single exposure when given an informativeutterance.

Distributional Modeling on a Diet: One-shot Word Learning from Text Only