论文标题
在分布语义上使用依赖解析进行几次学习
Using dependency parsing for few-shot learning in distributional semantics
论文作者
论文摘要
在这项工作中，我们探讨了在几乎没有学习的上下文中采用依赖解析信息的新颖想法，这是根据有限的上下文句子学习稀有词的含义的任务。首先，我们使用基于依赖关系的单词嵌入模型作为几次学习的背景空间。其次，我们介绍了两种几次学习方法，这些方法通过使用依赖项来增强添加剂基线模型。
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.
