README.md

---
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)](https://doi.org/10.1145/3582768.3582795).

💻 GitHub: [https://github.com/sebischair/Medical-Abstracts-TC-Corpus](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](https://doi.org/10.1145/3582768.3582795).

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}
}
```