readme: ---
  license: cc-by-nc-sa-4.0
  language:
  - en
  pipeline_tag: text-classification
  tags:
  - acronym disambiguation
  - acronym linking
  ---
  
  
  AcroBERT can do end-to-end acronym linking. Given a sentence, our framework first recognize acronyms by using [MadDog](https://github.com/amirveyseh/MadDog), and then disambiguate them by using AcroBERT:
  
  ```python
  from inference.acrobert important acronym_linker
  
  # input sentence with acronyms, the maximum length is 400 sub-tokens
  sentence = "This new genome assembly and the annotation are tagged as a RefSeq genome by NCBI."
  
  # mode = ['acrobert', 'pop']
  # AcroBERT has a better performance while the pop method is faster but with a low accuracy.
  results = acronym_linker(sentence, mode='acrobert')
  print(results)
  
  ## expected output: [('NCBI', 'National Center for Biotechnology Information')]
  ```
  
  Github: [https://github.com/tigerchen52/GLADIS](https://github.com/tigerchen52/GLADIS)
  
  Model: [https://zenodo.org/record/7568937#.Y9vtrXaZMuU]
  
  
  Apart from the AcroBERT, we constructed a new benchmark named GLADIS for accelerating the research on acronym disambiguation, which contains the below data:
  
  |  | Source  | Desc |
  |------|------------|------|
  | [Acronym Dictionary](https://zenodo.org/record/7568937#.Y9JiQXaZNPY) | [Pile](https://github.com/EleutherAI/the-pile) (MIT license), [Wikidata](https://www.wikidata.org/wiki/Help:Aliases), [UMLS](https://www.nlm.nih.gov/research/umls/index.html) |1.6 million acronyms and 6.4 million long forms|
  | [Three Datasets](https://zenodo.org/record/7568937#.Y9JiQXaZNPY) | [WikilinksNED Unseen](https://github.com/yasumasaonoe/ET4EL), [SciAD](https://github.com/amirveyseh/AAAI-21-SDU-shared-task-2-AD)(CC BY-NC-SA 4.0), [Medmentions](https://github.com/chanzuckerberg/MedMentions)(CC0 1.0)|three AD datasets that cover general, scientific, biomedical domains |
  | [A Pre-training Corpus](https://zenodo.org/record/7568937#.Y9JiQXaZNPY) | [Pile](https://github.com/EleutherAI/the-pile) (MIT license) | 180 million sentences with acronyms|
  
  
  
  
  ## usage
  1. git clone https://github.com/tigerchen52/GLADIS.git
  2. download the [acronym dictionary](https://zenodo.org/record/7568937#.Y9JiQXaZNPY) and [AcroBERT]((https://zenodo.org/record/7568937#.Y9JiQXaZNPY)), and put them into this path: `input/`
  3. use the function inference.acrobert.acronym_linker() to do end-to-end acronym linking.
  
  ## citation
  ```
  Lihu Chen, Gaël Varoquaux, & Fabian Suchanek. (2023, May 2).
  GLADIS: A General and Large Acronym Disambiguation Benchmark.
  The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL).
  ```