

AcroBERT can do end-to-end acronym linking (see the [Demo](https://huggingface.co/spaces/Lihuchen/AcroBERT) here). 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) | 160 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
```
Chen, Lihu, Gaël Varoquaux, and Fabian M. Suchanek. "GLADIS: A General and Large Acronym Disambiguation Benchmark." arXiv preprint arXiv:2302.01860 (2023).

```
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["acronym disambiguation", "acronym linking"], "pipeline_tag": "text-classification"}
acronym disambiguation acronym linking text-classification en license:cc-by-nc-sa-4.0 region:us
TAGS
#acronym disambiguation #acronym linking #text-classification #en #license-cc-by-nc-sa-4.0 #region-us 

AcroBERT can do end-to-end acronym linking (see the Demo here). Given a sentence, our framework first recognize acronyms by using MadDog, and then disambiguate them by using AcroBERT:


Github: URL


Model: [URL


Apart from the AcroBERT, we constructed a new benchmark named GLADIS for accelerating the research on acronym disambiguation, which contains the below data:


Source: Acronym Dictionary, Desc: Pile (MIT license), Wikidata, UMLS
Source: Three Datasets, Desc: WikilinksNED Unseen, SciAD(CC BY-NC-SA 4.0), Medmentions(CC0 1.0)
Source: A Pre-training Corpus, Desc: Pile (MIT license)


usage
-----


1. git clone URL
2. download the acronym dictionary and AcroBERT), and put them into this path: 'input/'
3. use the function inference.acrobert.acronym\_linker() to do end-to-end acronym linking.


citation
--------



TAGS
#acronym disambiguation #acronym linking #text-classification #en #license-cc-by-nc-sa-4.0 #region-us 

TAGS
#acronym disambiguation #acronym linking #text-classification #en #license-cc-by-nc-sa-4.0 #region-us 
