# Distributional Modeling on a Diet: One-shot Word Learning from Text Only

arXiv: https://arxiv.org/abs/1704.04550 · HTML: https://mlsanity.com/paper/1704.04550/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations](https://mlsanity.com/paper/2310.11634/) (2023)
- [Slot Attention-based Feature Filtering for Few-Shot Learning](https://mlsanity.com/paper/2508.09699/) (2025)
- [An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications](https://mlsanity.com/paper/2204.09885/) (2022)
- [Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities](https://mlsanity.com/paper/2210.17440/) (2022)
- [Stochastic-based Patch Filtering for Few-Shot Learning](https://mlsanity.com/paper/2508.10066/) (2025)

## Abstract (arXiv)

We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative. Our experiments show that our model can learn properties from a single exposure when given an informative utterance.