Towards One-Shot Learning for Text Classification using Inductive Logic
  Programming
Ghazal Afroozi Milani Daniel Cyrus Alireza Tamaddoni-Nezhad
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.