README.md

metadata

language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - text-classification
pretty_name: BioRel
dataset_info:
  features:
    - name: text
      dtype: string
    - name: relation
      dtype: string
    - name: h
      struct:
        - name: id
          dtype: string
        - name: name
          dtype: string
        - name: pos
          sequence: int64
    - name: t
      struct:
        - name: id
          dtype: string
        - name: name
          dtype: string
        - name: pos
          sequence: int64
  splits:
    - name: train
      num_bytes: 179296923
      num_examples: 534277
    - name: validation
      num_bytes: 38273878
      num_examples: 114506
    - name: test
      num_bytes: 38539441
      num_examples: 114565
  download_size: 107508802
  dataset_size: 256110242
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
tags:
  - biology
  - relation-classification
  - medical

Dataset Card for BioRel

Dataset Description

• Repository: https://drive.google.com/drive/folders/1vw2zIxdSoqT2QALDbRVG6loLsgi2doBG
• Paper: BioRel: towards large-scale biomedical relation extraction

Dataset Summary

BioRel Dataset Summary:

BioRel is a comprehensive dataset designed for biomedical relation extraction, leveraging the vast amount of electronic biomedical literature available. Developed using the Unified Medical Language System (UMLS) as a knowledge base and Medline articles as a corpus, BioRel utilizes Metamap for entity identification and linking, and employs distant supervision for relation labeling. The training set comprises 534,406 sentences, the validation set includes 218,669 sentences, and the testing set contains 114,515 sentences. This dataset supports both deep learning and statistical machine learning methods, providing a robust resource for training and evaluating biomedical relation extraction models. The original dataset is available here: https://drive.google.com/drive/folders/1vw2zIxdSoqT2QALDbRVG6loLsgi2doBG

We converted the dataset to the OpenNRE format using the following script: https://github.com/GDAMining/gda-extraction/blob/main/convert2opennre/convert_biorel2opennre.py

Languages

The language in the dataset is English.

Dataset Structure

Dataset Instances

An example of 'train' looks as follows:

{
  "text": "algal polysaccharide obtained from carrageenin protects 80 to 100 percent of chicken embryos against fatal infections with the lee strain of influenza virus .",
  "relation": "NA",
  "h": {
    "id": "C0032594",
    "name": "polysaccharide",
    "pos": [6, 20]
  },
  "t": {
    "id": "C0007289",
    "name": "carrageenin",
    "pos": [35, 46]
  }
}

Data Fields

• text: the text of this example, a string feature.
• h: head entity
  • id: identifier of the head entity, a string feature.
  • pos: character offsets of the head entity, a list of int32 features.
  • name: head entity text, a string feature.
• t: tail entity
  • id: identifier of the tail entity, a string feature.
  • pos: character offsets of the tail entity, a list of int32 features.
  • name: tail entity text, a string feature.
• relation: a class label.

Citation

BibTeX:

@article{xing2020biorel,
  title={BioRel: towards large-scale biomedical relation extraction},
  author={Xing, Rui and Luo, Jie and Song, Tengwei},
  journal={BMC bioinformatics},
  volume={21},
  pages={1--13},
  year={2020},
  publisher={Springer}
}

APA:

• Xing, R., Luo, J., & Song, T. (2020). BioRel: towards large-scale biomedical relation extraction. BMC bioinformatics, 21, 1-13.

Dataset Card Authors

@phucdev