README.md

---
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 distantly supervised biomedical relation extraction (Bio-DSRE) corpus obtained by aligning the PubMed MEDLINE abstracts from 2019 with the SNOMED-CT knowledge graph (KG) derived from the UMLS Metathesaurus 2019.
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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

### Data Instances

An example of 'train' looks as follows:
```json
{
    "text": "Urethral stones are rarely formed primarily in the urethra and are usually associated with urethral strictures or diverticula .", 
    "h": {"id": "C0041967", "pos": [51, 58], "name": "urethra"}, 
    "t": {"id": "C0041974", "pos": [91, 110], "name": "urethral strictures"}, 
    "relation": "finding_site_of"
}
```

### 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.


## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]


#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```tex
@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](https://github.com/phucdev)

## Dataset Card Contact

[@phucdev](https://github.com/phucdev)