metadata

license: cc-by-nc-nd-4.0
task_categories:
  - question-answering
task_ids:
  - open-domain-qa
  - extractive-qa
language:
  - tr
tags:
  - medical
pretty_name: MedTurkQuAD
size_categories:
  - 1K<n<10K
dataset_info:
  total_examples: 8200
  total_paragraphs: 875
  source_articles: 618
source_datasets:
  - original
paperswithcode_id: medturkquad-medical-turkish-question

MedTurkQuAD: Medical Turkish Question-Answering Dataset

MedTurkQuAD is a dataset specifically designed for question-answering (QA) tasks in the medical domain in Turkish. It contains context paragraphs derived from medical texts, paired with questions and answers related to specific diseases or medical issues. For more details about the dataset, methodology, and experiments, you can refer to the corresponding research paper.

Dataset Overview

Number of Paragraphs: 875
Number of QA Pairs: 8,200
Sources: 618 medical articles (110 Wikipedia, 508 Thesis in medicine)
Languages: Turkish

Dataset Structure

The dataset is divided into three subsets for training, validation, and testing:

Split	Number of Paragraphs	Number of QA Pairs
Training	700	6560
Validation	87	820
Testing	88	820

How to Use

This dataset can be used with libraries such as 🤗 Datasets or pandas. Below are examples of the use of the dataset:

from datasets import load_dataset

ds = load_dataset("incidelen/MedTurkQuAD")

import pandas as pd

splits = {'train': 'train.json', 'validation': 'validation.json', 'test': 'test.json'}
df = pd.read_json("hf://datasets/incidelen/MedTurkQuAD/" + splits["train"])

Citation

If you use this dataset, please cite the following paper:

@INPROCEEDINGS{10711128,
  author={İncidelen, Mert and Aydoğan, Murat},
  booktitle={2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)}, 
  title={Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches}, 
  year={2024},
  volume={},
  number={},
  pages={1-4},
  keywords={Training;Adaptation models;Natural languages;Focusing;Encyclopedias;Transformers;Data models;Internet;Online services;Text processing;Natural Language Processing;Medical Domain;BERTurk;Question-Answering},
  doi={10.1109/IDAP64064.2024.10711128}}

Acknowledgments

Special thanks to maydogan for their contributions and support in the development of this dataset.