MedMentions

license: apache-2.0
configs:
  - config_name: Original
    data_files:
      - split: train
        path: original_data/train*
      - split: validation
        path: original_data/validation*
      - split: test
        path: original_data/test*
  - config_name: Pre-Processed
    data_files:
      - split: train
        path: processed_data/train*
      - split: validation
        path: processed_data/validation*
      - split: test
        path: processed_data/test*
  - config_name: Training (No Context;No Memory)
    data_files:
      - split: train
        path: training_data/train_tfidf_short.parquet
      - split: validation
        path: training_data/validation_tfidf_short.parquet
      - split: test
        path: training_data/test_tfidf_short.parquet
  - config_name: Training (No Context;With Memory)
    data_files:
      - split: train
        path: training_data/train_tfidf_hybrid_short.parquet
      - split: validation
        path: training_data/validation_tfidf_hybrid_short.parquet
      - split: test
        path: training_data/test_tfidf_hybrid_short.parquet
  - config_name: Training (With Context;No Memory)
    data_files:
      - split: train
        path: training_data/train_tfidf_hybrid_medium.parquet
      - split: validation
        path: training_data/validation_tfidf_hybrid_medium.parquet
      - split: test
        path: training_data/test_tfidf_hybrid_medium.parquet
  - config_name: Training (With Context;With Memory)
    data_files:
      - split: train
        path: training_data/train_tfidf_hybrid_long.parquet
      - split: validation
        path: training_data/validation_tfidf_hybrid_long.parquet
      - split: test
        path: training_data/test_tfidf_hybrid_long.parquet
  - config_name: Training (With Context;With Memory) Densed
    data_files:
      - split: train
        path: training_data/train_tfidf_long.parquet
      - split: validation
        path: training_data/validation_tfidf_long.parquet
      - split: test
        path: training_data/test_tfidf_long.parquet
task_categories:
  - text-classification
  - text-generation
  - zero-shot-classification
  - token-classification
language:
  - en
tags:
  - UMLS
  - entity_linking
  - biomedical
size_categories:
  - 1K<n<10K

Dataset Card for MedMentions

Dataset Description

Homepage: https://github.com/chanzuckerberg/MedMentions
Pubmed: True
Public: True
Tasks: NED,NER

MedMentions is a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.

Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected from among papers released on PubMed in 2016, that were in the biomedical field, published in the English language, and had both a Title and an Abstract.

Annotators: We recruited a team of professional annotators with rich experience in biomedical content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in these papers.

Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain insight on the annotation quality of MedMentions, we randomly selected eight papers from the annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not participate in the annotation task then each reviewed four papers. The agreement between Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%.

Citation Information

@misc{mohan2019medmentions,
      title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts},
      author={Sunil Mohan and Donghui Li},
      year={2019},
      eprint={1902.09476},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}