Datasets:
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
Data Instances
An example of 'train' looks as follows:
{
"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, astring
feature.h
: head entityid
: identifier of the head entity, astring
feature.pos
: character offsets of the head entity, a list ofint32
features.name
: head entity text, astring
feature.
t
: tail entityid
: identifier of the tail entity, astring
feature.pos
: character offsets of the tail entity, a list ofint32
features.name
: tail entity text, astring
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.