BLURB / README.md
Dr. Jorge Abreu Vicente
First upload of dataset card.
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metadata
license: apache-2.0

annotations_creators:

  • expert-generated language_creators:
  • expert-generated languages:
  • en licenses:
  • unknown multilinguality:
  • monolingual paperswithcode_id: null pretty_name: BLURB (Biomedical Language Understanding and Reasoning Benchmark.) size_categories:
  • 10K<n<100K source_datasets:
  • original task_categories:
  • structure-prediction
  • question-answering
  • text-scoring
  • text-classification task_ids:
  • named-entity-recognition
  • parsing
  • closed-domain-qa
  • semantic-similarity-scoring
  • text-scoring-other-sentence-similrity
  • topic-classification---

Dataset Card for [Dataset Name]

Table of Contents

Dataset Description

Dataset Summary

BLURB is a collection of resources for biomedical natural language processing. In general domains, such as newswire and the Web, comprehensive benchmarks and leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. In biomedicine, however, such resources are ostensibly scarce. In the past, there have been a plethora of shared tasks in biomedical NLP, such as BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These efforts have played a significant role in fueling interest and progress by the research community, but they typically focus on individual tasks. The advent of neural language models, such as BERT provides a unifying foundation to leverage transfer learning from unlabeled text to support a wide range of NLP applications. To accelerate progress in biomedical pretraining strategies and task-specific methods, it is thus imperative to create a broad-coverage benchmark encompassing diverse biomedical tasks.

Inspired by prior efforts toward this direction (e.g., BLUE), we have created BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP applications, as well as a leaderboard for tracking progress by the community. BLURB includes thirteen publicly available datasets in six diverse tasks. To avoid placing undue emphasis on tasks with many available datasets, such as named entity recognition (NER), BLURB reports the macro average across all tasks as the main score. The BLURB leaderboard is model-agnostic. Any system capable of producing the test predictions using the same training and development data can participate. The main goal of BLURB is to lower the entry barrier in biomedical NLP and help accelerate progress in this vitally important field for positive societal and human impact.

Supported Tasks and Leaderboards

Dataset Task Train Dev Test Evaluation Metrics Added
BC5-chem NER 5203 5347 5385 F1 entity-level Yes
BC5-disease NER 4182 4244 4424 F1 entity-level Yes
NCBI-disease NER 5134 787 960 F1 entity-level Yes
BC2GM NER 15197 3061 6325 F1 entity-level Yes
JNLPBA NER 46750 4551 8662 F1 entity-level Yes
EBM PICO PICO 339167 85321 16364 Macro F1 word-level No
ChemProt Relation Extraction 18035 11268 15745 Micro F1 No
DDI Relation Extraction 25296 2496 5716 Micro F1 No
GAD Relation Extraction 4261 535 534 Micro F1 No
BIOSSES Sentence Similarity 64 16 20 Pearson No
HoC Document Classification 1295 186 371 Average Micro F1 No
PubMedQA Question Answering 450 50 500 Accuracy No
BioASQ Question Answering 670 75 140 Accuracy No

Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB. This is something to be checked.

Languages

English from biomedical texts

Dataset Structure

Data Instances

  • NER
  • PICO
  • Relation Extraction
  • Sentence Similarity
  • Document Classification
  • Question Answering

Data Fields

  • NER
    • id, ner_tags, tokens
  • PICO
  • Relation Extraction
  • Sentence Similarity
  • Document Classification
  • Question Answering

Data Splits

Shown in the table of supported tasks.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @github-username for adding this dataset.