| | --- |
| | language: |
| | - en |
| | bigbio_language: |
| | - English |
| | license: other |
| | multilinguality: monolingual |
| | bigbio_license_shortname: MIXED |
| | pretty_name: BLURB |
| | homepage: https://microsoft.github.io/BLURB/tasks.html |
| | bigbio_pubmed: true |
| | bigbio_public: true |
| | bigbio_tasks: |
| | - NAMED_ENTITY_RECOGNITION |
| | --- |
| | |
| |
|
| | # Dataset Card for BLURB |
| |
|
| | ## Dataset Description |
| |
|
| | - **Homepage:** https://microsoft.github.io/BLURB/tasks.html |
| | - **Pubmed:** True |
| | - **Public:** True |
| | - **Tasks:** NER |
| |
|
| | 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. |
| |
|
| | This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits. |
| |
|
| |
|
| | ## Citation Information |
| |
|
| | ``` |
| | @article{gu2021domain, |
| | title = { |
| | Domain-specific language model pretraining for biomedical natural |
| | language processing |
| | }, |
| | 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 |
| | }, |
| | year = 2021, |
| | journal = {ACM Transactions on Computing for Healthcare (HEALTH)}, |
| | publisher = {ACM New York, NY}, |
| | volume = 3, |
| | number = 1, |
| | pages = {1--23} |
| | } |
| | ``` |
| |
|
