metadata

language:
  - en
task_categories:
  - text-classification
pretty_name: MedDistant19
dataset_info:
  features:
    - name: text
      dtype: string
    - name: h
      struct:
        - name: id
          dtype: string
        - name: pos
          list: int32
        - name: name
          dtype: string
    - name: t
      struct:
        - name: id
          dtype: string
        - name: pos
          list: int32
        - name: name
          dtype: string
    - name: relation
      dtype:
        class_label:
          names:
            '0': NA
            '1': active_ingredient_of
            '2': associated_finding_of
            '3': associated_morphology_of
            '4': causative_agent_of
            '5': cause_of
            '6': component_of
            '7': direct_device_of
            '8': direct_morphology_of
            '9': direct_procedure_site_of
            '10': direct_substance_of
            '11': finding_site_of
            '12': focus_of
            '13': indirect_procedure_site_of
            '14': interpretation_of
            '15': interprets
            '16': is_modification_of
            '17': method_of
            '18': occurs_after
            '19': procedure_site_of
            '20': uses_device
            '21': uses_substance
  splits:
    - name: train
      num_bytes: 114832958
      num_examples: 450071
    - name: validation
      num_bytes: 10158868
      num_examples: 39434
    - name: test
      num_bytes: 23816522
      num_examples: 91568
  download_size: 85782402
  dataset_size: 148808348
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
tags:
  - medical

Dataset Card for MedDistant19

Dataset Description

Repository: https://github.com/suamin/MedDistant19
Paper: https://aclanthology.org/2022.coling-1.198/

Dataset Summary

MedDistant19 is a more accurate benchmark for broad-coverage distantly supervised biomedical relation extraction that addresses these shortcomings and is obtained by aligning the MEDLINE abstracts with the widely used SNOMED Clinical Terms knowledge base. For more details, please refer to the paper: https://aclanthology.org/2022.coling-1.198/

Before Downloading: To use this data, you must have signed the UMLS agreement. The UMLS agreement requires those who use the UMLS to file a brief report once a year to summarize their use of the UMLS. It also requires the acknowledgment that the UMLS contains copyrighted material and that those copyright restrictions be respected. The UMLS agreement requires users to agree to obtain agreements for EACH copyrighted source prior to its use within a commercial or production application. See https://www.nlm.nih.gov/databases/umls.html

Languages

The language in the dataset is English.

Dataset Structure

Data Instances

An example of 'train' looks as follow:

{
  'text': 'In spite of multiple treatment regimens consisting of surgical resection , radiation therapy , and multi-agent chemotherapy , the prognosis is very poor .',
  'h': {
    'id': 'C0015252',
    'start': 54,
    'end': 72,
    'name': 'surgical resection'
  },
  't': {
    'id': 'C0033325',
    'start': 130,
    'end': 139,
    'name': 'prognosis'
  },
  'relation': 0
}

Data Fields

text: the text of this example, a string feature.
h: head entity
- id: identifier of the head entity, a string feature.
- start: character off start of the head entity, a int32 feature.
- end: character off end of the head entity, a int32 feature.
- name: head entity text, a string feature.
t: tail entity
- id: identifier of the tail entity, a string feature.
- start: character off start of the tail entity, a int32 feature.
- end: character off end of the tail entity, a int32 feature.
- name: tail entity text, a string feature.
relation: a class label.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Data Collection and Processing

[More Information Needed]

Who are the source data producers?

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

BibTeX:

@inproceedings{amin-etal-2022-meddistant19,
    title = "{M}ed{D}istant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction",
    author = "Amin, Saadullah and Minervini, Pasquale and Chang, David and Stenetorp, Pontus and Neumann, G{\"u}nter",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.198",
    pages = "2259--2277",
}

APA:

Amin, S., Minervini, P., Chang, D., Stenetorp, P., & Neumann, G. (2022). Meddistant19: towards an accurate benchmark for broad-coverage biomedical relation extraction. arXiv preprint arXiv:2204.04779.

Dataset Card Authors

@phucdev

Dataset Card Contact

@phucdev