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Distributional semantics models are known to struggle with small data. It isgenerally accepted that in order to learn 'a good vector' for a word, a modelmust have sufficient examples of its usage. This contradicts the fact thathumans can guess the meaning of a word from a few occurrences only. In thispaper, we show that a neural language model such as Word2Vec only necessitatesminor modifications to its standard architecture to learn new terms from tinydata, using background knowledge from a previously learnt semantic space. Wetest our model on word definitions and on a nonce task involving 2-6 sentences'worth of context, showing a large increase in performance over state-of-the-artmodels on the definitional task.

High-risk learning: acquiring new word vectors from tiny data