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---
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license: apache-2.0
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annotations_creators:
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- expert-generated
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language_creators:
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- expert-generated
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languages:
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- en
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licenses:
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- unknown
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multilinguality:
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- monolingual
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paperswithcode_id: null
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pretty_name: BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
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size_categories:
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- 10K<n<100K
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source_datasets:
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- original
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task_categories:
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- structure-prediction
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- question-answering
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- text-scoring
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- text-classification
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task_ids:
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- named-entity-recognition
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- parsing
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- closed-domain-qa
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- semantic-similarity-scoring
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- text-scoring-other-sentence-similrity
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- topic-classification---
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---
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# Dataset Card for BLURB
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage: https://microsoft.github.io/BLURB/index.html**
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- **Repository:**
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- **Paper: https://arxiv.org/pdf/2007.15779.pdf**
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- **Leaderboard: https://microsoft.github.io/BLURB/leaderboard.html**
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- **Point of Contact:**
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### Dataset Summary
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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.
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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.
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### Supported Tasks and Leaderboards
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| **Dataset** | **Task** | **Train** | **Dev** | **Test** | **Evaluation Metrics** | **Added** |
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|:------------:|:-----------------------:|:---------:|:-------:|:--------:|:----------------------:|-----------|
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| BC5-chem | NER | 5203 | 5347 | 5385 | F1 entity-level | Yes |
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| BC5-disease | NER | 4182 | 4244 | 4424 | F1 entity-level | Yes |
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| NCBI-disease | NER | 5134 | 787 | 960 | F1 entity-level | Yes |
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| BC2GM | NER | 15197 | 3061 | 6325 | F1 entity-level | Yes |
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| JNLPBA | NER | 46750 | 4551 | 8662 | F1 entity-level | Yes |
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| EBM PICO | PICO | 339167 | 85321 | 16364 | Macro F1 word-level | No |
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| ChemProt | Relation Extraction | 18035 | 11268 | 15745 | Micro F1 | No |
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| DDI | Relation Extraction | 25296 | 2496 | 5716 | Micro F1 | No |
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| GAD | Relation Extraction | 4261 | 535 | 534 | Micro F1 | No |
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| BIOSSES | Sentence Similarity | 64 | 16 | 20 | Pearson | No |
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| HoC | Document Classification | 1295 | 186 | 371 | Average Micro F1 | No |
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| PubMedQA | Question Answering | 450 | 50 | 500 | Accuracy | No |
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| BioASQ | Question Answering | 670 | 75 | 140 | Accuracy | No |
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Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB.
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This is something to be checked.
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### Languages
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English from biomedical texts
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## Dataset Structure
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### Data Instances
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* **NER**
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```json
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{
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'id': 0,
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'tokens': [ "DPP6", "as", "a", "candidate", "gene", "for", "neuroleptic", "-", "induced", "tardive", "dyskinesia", "." ]
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'ner_tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
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}
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```
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* **PICO**
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```json
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{
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'TBD'
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}
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```
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* **Relation Extraction**
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```json
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{
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'TBD'
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}
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```
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* **Sentence Similarity**
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```json
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{
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'TBD'
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}
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```
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* **Document Classification**
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```json
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{
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'TBD'
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}
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```
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* **Question Answering**
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```json
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{
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'TBD'
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}
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```
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### Data Fields
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* **NER**
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* id, ner_tags, tokens
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* **PICO**
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* To be added
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* **Relation Extraction**
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* To be added
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* **Sentence Similarity**
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* To be added
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* **Document Classification**
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* To be added
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* **Question Answering**
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* To be added
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### Data Splits
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Shown in the table of supported tasks.
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## Dataset Creation
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### Curation Rationale
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All the datasets have been obtained and annotated by experts in the biomedical domain. Check the different citations for further details.
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### Source Data
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All the datasets have been obtained and annotated by experts in the biomedical domain. Check the different citations for further details.
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[More Information Needed]
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### Annotations
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All the datasets have been obtained and annotated by experts in the biomedical domain. Check the different citations for further details.
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## Additional Information
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### Dataset Curators
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All the datasets have been obtained and annotated by experts in thebiomedical domain. Check the different citations for further details.
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### Licensing Information
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To be checked in the different datasets.
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### Citation Information
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```json
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{
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"blurb": """\
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@article{2022,
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title={Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing},
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volume={3},
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ISSN={2637-8051},
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url={http://dx.doi.org/10.1145/3458754},
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DOI={10.1145/3458754},
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number={1},
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journal={ACM Transactions on Computing for Healthcare},
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publisher={Association for Computing Machinery (ACM)},
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author={Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, Jianfeng and Poon, Hoifung},
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year={2022},
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month={Jan},
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pages={1–23}
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}
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""",
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"BC5CDR-chem-IOB": """@article{article,
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author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
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year = {2016},
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month = {05},
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pages = {baw068},
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title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
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volume = {2016},
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journal = {Database},
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doi = {10.1093/database/baw068}
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}""",
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"BC5CDR-disease-IOB":"""@article{article,
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author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
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year = {2016},
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month = {05},
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pages = {baw068},
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title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
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volume = {2016},
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journal = {Database},
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doi = {10.1093/database/baw068}
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}""",
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"BC2GM-IOB":"""@article{article,
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author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
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year = {2008},
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month = {09},
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pages = {S2},
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title = {Overview of BioCreative II gene mention recognition},
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volume = {9 Suppl 2},
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journal = {Genome biology},
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doi = {10.1186/gb-2008-9-s2-s2}
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}""",
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"NCBI-disease-IOB":"""@article{10.5555/2772763.2772800,
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author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
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title = {NCBI Disease Corpus},
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year = {2014},
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issue_date = {February 2014},
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publisher = {Elsevier Science},
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address = {San Diego, CA, USA},
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volume = {47},
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number = {C},
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issn = {1532-0464},
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abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
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journal = {J. of Biomedical Informatics},
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month = {feb},
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pages = {1–10},
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numpages = {10}}""",
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"JNLPBA":"""@inproceedings{collier-kim-2004-introduction,
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title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
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author = "Collier, Nigel and
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Kim, Jin-Dong",
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booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
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month = aug # " 28th and 29th",
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year = "2004",
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address = "Geneva, Switzerland",
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publisher = "COLING",
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url = "https://aclanthology.org/W04-1213",
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pages = "73--78",
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}""",
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}
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```
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### Contributions
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This dataset has been uploaded and generated by Dr. Jorge Abreu Vicente.
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Thanks to [@GamalC](https://github.com/GamalC) for uploading the NER datasets to GitHub, from where I got them.
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I am not part of the team that generated BLURB. This dataset is intended to help researchers to usethe BLURB benchmarking for NLP in Biomedical NLP. |
