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.
@article{arxiv.2205.06168,
title = {Using dependency parsing for few-shot learning in distributional semantics},
author = {Stefania Preda and Guy Emerson},
journal= {arXiv preprint arXiv:2205.06168},
year = {2022}
}