GLADIS: A General and Large Acronym Disambiguation Benchmark (Long paper at EACL 23) under the CC0-1.0 license.
In this paper, we propose a new Benchmark named Gladis and an acronym linking system named AcroBERT. We have a demo on Huggingface, and the below example shows that the result for the a sentence with the arcronym "NCBI":
To accelerate the research on acronym disambiguation, we constructed a new benchmark named GLADIS and the pre-trained AcroBERT. The table below shows the main components in our new benchmark.
| Source | Description | |
|---|---|---|
| Acronym Dictionary | Pile (MIT license), Wikidata, UMLS | 1.6 million acronyms and 6.4 million long forms |
| Three Datasets | WikilinksNED Unseen, SciAD(CC BY-NC-SA 4.0), Medmentions(CC0 1.0) | three AD datasets that cover general, scientific, biomedical domains |
| A Pre-training Corpus | Pile (MIT license) | 160 million sentences with acronyms |
| AcroBERT | BERT-based model | the first pre-trained language model for general acronym disambiguation |
AcroBERT can do end-to-end acronym linking. Given a sentence, our framework first recognize acronyms by using MadDog (CC BY-NC-SA 4.0), and then disambiguate them by using AcroBERT:
from inference.acrobert import 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')]The new benchmark constructed in this paper is located in input/dataset.
The acronym dictionary is supposed to stored in this file: input/dataset/acronym_kb.json, which includes 1.5M acronyms
and 6.4M long forms.
However, due to the size limit of the upload files, you have to download the dictionary (with the AcroBERT model together) from this link:
dictionary and model.
After downloading, decompress it and put the two files to this path input/
First you can use -help to show the arguments
python train.py -help
Once completing the data preparation and environment setup, we can train the model via acrobert.py.
python acrobert.py -pre_train_path ../input/pre_train_sample.txt
The entire pre-training needs two weeks on a single NVIDIA Tesla V100S PCIe 32 GB Specs.
We provide a one-line command to reproduce the scores in Table A1,
which is the easiest one to reproduce, and you can see the scores after only several minutes.
The needed test sets are in this path /evaluation/test_set, and you can find three evaluation sets.
The corresponding dictionaries are in this path /evaluation/dict.
We also provided the AcroBERT model file in this path /input/acrobert.pt.
Then the scores can be obtained by using the following command:
python acrobert.py -mode evaluation
Finally, you can see the results
F1: [88.8, 58.0, 67.5], ACC: [93.7, 72.0, 65.3]
python acrobert.py -mode finetuning -learning_rate 1e-6 -hard_neg_numbers 1 -lr_decay 0.6
@inproceedings{chen2023gladis,
title={GLADIS: A General and Large Acronym Disambiguation Benchmark},
author={Chen, Lihu and Varoquaux, Ga{\"e}l and Suchanek, Fabian M},
booktitle={EACL 2023-The 17th Conference of the European Chapter of the Association for Computational Linguistics},
year={2023}
}
This work was partially funded by ANR-20-CHIA0012-01 (“NoRDF”).
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