Using dependency parsing for few-shot learning in distributional semantics
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 12 May 2022.
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References · 11
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2021True Few-Shot Learning with Language Modelscited by 343
- 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
- 2019Distributional Semantics and Linguistic Theorycited by 53
- 2018A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectorscited by 17
- 2017High-risk learning: acquiring new word vectors from tiny datacited by 13
- 2018Learning Semantic Representations for Novel Words: Leveraging Both Form and Contextcited by 8
- 2020What are the Goals of Distributional Semantics?cited by 8
- 2021Representing Syntax and Composition with Geometric Transformationscited by 3
- 2019Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Modelscited by 1
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