  In this work, we explore the novel idea of employing dependency parsing
information in the context of few-shot learning, the task of learning the
meaning of a rare word based on a limited amount of context sentences. Firstly,
we use dependency-based word embedding models as background spaces for few-shot
learning. Secondly, we introduce two few-shot learning methods which enhance
the additive baseline model by using dependencies.
