README.md

metadata

dataset_info:
  - config_name: default
    features:
      - name: condition_label
        dtype: int64
      - name: medical_abstract
        dtype: string
    splits:
      - name: train
        num_bytes: 14334753
        num_examples: 11550
      - name: test
        num_bytes: 3606846
        num_examples: 2888
    download_size: 9606491
    dataset_size: 17941599
  - config_name: labels
    features:
      - name: condition_label
        dtype: int64
      - name: condition_name
        dtype: string
    splits:
      - name: train
        num_bytes: 171
        num_examples: 5
    download_size: 1611
    dataset_size: 171
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
  - config_name: labels
    data_files:
      - split: train
        path: labels/train-*
license: cc-by-sa-3.0
task_categories:
  - text-classification
language:
  - en
pretty_name: Medical Abstracts Text Classification Dataset
size_categories:
  - 10K<n<100K
tags:
  - medical

Medical Abstracts Text Classification Dataset

This repository contains a medical abstracts dataset, describing 5 different classes of patient conditions. The dataset can be used for text classification.

📄 Paper: Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches (NLPIR 2022).

💻 GitHub: https://github.com/sebischair/Medical-Abstracts-TC-Corpus

Summary of the medical abstracts dataset:

• The default subset contains the train and test splits with numerical class labels.
• The labels subset contains the textual names of the numerical class labels.

Class name	#training	#test	Total
Neoplasms	2530	633	3163
Digestive system diseases	1195	299	1494
Nervous system diseases	1540	385	1925
Cardiovascular diseases	2441	610	3051
General pathological conditions	3844	961	4805
Total	11550	2888	14438

Citation information

This dataset was created during the writing of our paper titled Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches.

When citing this medical abstracts dataset in academic papers and theses, please use the following BibTeX entry:

@inproceedings{10.1145/3582768.3582795,
author = {Schopf, Tim and Braun, Daniel and Matthes, Florian},
title = {Evaluating Unsupervised Text Classification: Zero-Shot and Similarity-Based Approaches},
year = {2023},
isbn = {9781450397629},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3582768.3582795},
doi = {10.1145/3582768.3582795},
abstract = {Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE [7] and SBERT-based [26] baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.},
booktitle = {Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval},
pages = {6–15},
numpages = {10},
keywords = {Zero-shot Text Classification, Natural Language Processing, Unsupervised Text Classification},
location = {Bangkok, Thailand},
series = {NLPIR '22}
}