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

• URL: http://arxiv.org/abs/2308.15885v1
• Date: Wed, 30 Aug 2023 09:04:06 GMT
• Title: Towards One-Shot Learning for Text Classification using Inductive Logic Programming
• Authors: Ghazal Afroozi Milani (University of Surrey), Daniel Cyrus (University of Surrey), Alireza Tamaddoni-Nezhad (University of Surrey)
• Abstract summary: In this paper, we explore an Inductive Logic Programming approach for one-shot text classification. Results indicate that MIL can learn text classification rules from a small number of training examples.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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