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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: other |
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multilinguality: monolingual |
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bigbio_license_shortname: MIXED |
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pretty_name: BLURB |
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homepage: https://microsoft.github.io/BLURB/tasks.html |
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bigbio_pubmed: true |
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bigbio_public: true |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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--- |
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# Dataset Card for BLURB |
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## Dataset Description |
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- **Homepage:** https://microsoft.github.io/BLURB/tasks.html |
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- **Pubmed:** True |
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- **Public:** True |
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- **Tasks:** NER |
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BLURB is a collection of resources for biomedical natural language processing. |
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In general domains, such as newswire and the Web, comprehensive benchmarks and |
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leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. |
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In biomedicine, however, such resources are ostensibly scarce. In the past, |
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there have been a plethora of shared tasks in biomedical NLP, such as |
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BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These |
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efforts have played a significant role in fueling interest and progress by the |
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research community, but they typically focus on individual tasks. The advent of |
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neural language models, such as BERT provides a unifying foundation to leverage |
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transfer learning from unlabeled text to support a wide range of NLP |
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applications. To accelerate progress in biomedical pretraining strategies and |
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task-specific methods, it is thus imperative to create a broad-coverage |
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benchmark encompassing diverse biomedical tasks. |
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Inspired by prior efforts toward this direction (e.g., BLUE), we have created |
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BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). |
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BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP |
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applications, as well as a leaderboard for tracking progress by the community. |
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BLURB includes thirteen publicly available datasets in six diverse tasks. To |
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avoid placing undue emphasis on tasks with many available datasets, such as |
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named entity recognition (NER), BLURB reports the macro average across all tasks |
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as the main score. The BLURB leaderboard is model-agnostic. Any system capable |
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of producing the test predictions using the same training and development data |
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can participate. The main goal of BLURB is to lower the entry barrier in |
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biomedical NLP and help accelerate progress in this vitally important field for |
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positive societal and human impact. |
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This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits. |
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## Citation Information |
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|
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``` |
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@article{gu2021domain, |
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title = { |
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Domain-specific language model pretraining for biomedical natural |
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language processing |
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}, |
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author = { |
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Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and |
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Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao, |
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Jianfeng and Poon, Hoifung |
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}, |
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year = 2021, |
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journal = {ACM Transactions on Computing for Healthcare (HEALTH)}, |
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publisher = {ACM New York, NY}, |
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volume = 3, |
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number = 1, |
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pages = {1--23} |
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} |
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``` |
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