# Using dependency parsing for few-shot learning in distributional semantics

arXiv: https://arxiv.org/abs/2205.06168 · HTML: https://mlsanity.com/paper/2205.06168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 11 of 11)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Distributional Semantics and Linguistic Theory](https://mlsanity.com/paper/1905.01896/) (2019)
- [A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors](https://mlsanity.com/paper/1805.05388/) (2018)
- [High-risk learning: acquiring new word vectors from tiny data](https://mlsanity.com/paper/1707.06556/) (2017)
- [Learning Semantic Representations for Novel Words: Leveraging Both Form and Context](https://mlsanity.com/paper/1811.03866/) (2018)
- [What are the Goals of Distributional Semantics?](https://mlsanity.com/paper/2005.02982/) (2020)
- [Representing Syntax and Composition with Geometric Transformations](https://mlsanity.com/paper/2106.01904/) (2021)
- [Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models](https://mlsanity.com/paper/1910.00275/) (2019)

## 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.