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--- |
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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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size_categories: |
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- 10M<n<100M |
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source_datasets: |
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- original |
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task_categories: |
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- other |
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task_ids: |
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- other-other-disambiguation |
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--- |
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# Dataset Card Creation Guide |
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## 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](#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-instances) |
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- [Data Splits](#data-instances) |
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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:** []() |
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- **Repository:** [https://github.com/BruceWen120/medal]() |
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- **Paper:** [https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/]() |
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- **Dataset (Kaggle):** [https://www.kaggle.com/xhlulu/medal-emnlp]() |
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- **Dataset (Zenodo):** [https://zenodo.org/record/4265632]() |
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- **Pretrained model:** [https://huggingface.co/xhlu/electra-medal]() |
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- **Leaderboard:** []() |
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- **Point of Contact:** []() |
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### Dataset Summary |
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A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate |
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### Supported Tasks and Leaderboards |
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Medical abbreviation disambiguation |
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### Languages |
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English (en) |
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## Dataset Structure |
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[More Information Needed] |
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### Data Instances |
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[More Information Needed] |
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### Data Fields |
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[More Information Needed] |
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### Data Splits |
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[More Information Needed] |
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## Dataset Creation |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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[More Information Needed] |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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[More Information Needed] |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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[More Information Needed] |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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``` |
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@inproceedings{wen-etal-2020-medal, |
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title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining", |
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author = "Wen, Zhi and |
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Lu, Xing Han and |
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Reddy, Siva", |
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booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15", |
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pages = "130--135", |
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abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.", |
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} |
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``` |
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### Contributions |
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Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. |
