{
  "id": "2308.15885",
  "version": "v1",
  "published": "2023-08-30T09:04:06.000Z",
  "updated": "2023-08-30T09:04:06.000Z",
  "title": "Towards One-Shot Learning for Text Classification using Inductive Logic Programming",
  "authors": [
    "Ghazal Afroozi Milani",
    "Daniel Cyrus",
    "Alireza Tamaddoni-Nezhad"
  ],
  "comment": "In Proceedings ICLP 2023, arXiv:2308.14898",
  "journal": "EPTCS 385, 2023, pp. 69-79",
  "doi": "10.4204/EPTCS.385.9",
  "categories": [
    "cs.LG",
    "cs.CL",
    "cs.LO"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-08-30T09:04:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "one-shot learning",
      "learn text classification rules",
      "one-shot text classification",
      "common-sense background knowledge",
      "perform personalised tasks"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}