# Towards One-Shot Learning for Text Classification using Inductive Logic Programming

arXiv: https://arxiv.org/abs/2308.15885 · HTML: https://mlsanity.com/paper/2308.15885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [News Category Dataset](https://mlsanity.com/paper/2209.11429/) (2022)
- [Dimensions of Commonsense Knowledge](https://mlsanity.com/paper/2101.04640/) (2021)
- [Inductive Learning on Commonsense Knowledge Graph Completion](https://mlsanity.com/paper/2009.09263/) (2020)
- [Logical reduction of metarules](https://mlsanity.com/paper/1907.10952/) (2019)
- [Semantic Networks for Engineering Design: A Survey](https://mlsanity.com/paper/2012.07060/) (2020)
- [Meta-Interpretive Learning as Metarule Specialisation](https://mlsanity.com/paper/2106.07464/) (2021)

## Abstract (arXiv)

With the ever-increasing potential of AI to perform personalised tasks, it is becoming essential to develop new machine learning techniques which are data-efficient and do not require hundreds or thousands of training data. In this paper, we explore an Inductive Logic Programming approach for one-shot text classification. In particular, we explore the framework of Meta-Interpretive Learning (MIL), along with using common-sense background knowledge extracted from ConceptNet. Results indicate that MIL can learn text classification rules from a small number of training examples. Moreover, the higher complexity of chosen examples, the higher accuracy of the outcome.