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Tool for disambiguating acronyms and abbreviations in text for NLP applications

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# Adeft
[![DOI](https://joss.theoj.org/papers/10.21105/joss.01708/status.svg)](https://doi.org/10.21105/joss.01708)
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Adeft (Acromine based Disambiguation of Entities From Text context) is a
utility for building models to disambiguate acronyms and other abbreviations of
biological terms in the scientific literature. It makes use of an
implementation of the [Acromine](http://www.chokkan.org/research/acromine/)
algorithm developed by the [NaCTeM](http://www.nactem.ac.uk/index.php) at the
University of Manchester to identify possible longform expansions for
shortforms in a text corpus.  It allows users to build disambiguation models to
disambiguate shortforms based on their text context. A growing number of
pretrained disambiguation models are publicly available to download through
adeft.

#### Citation

If you use Adeft in your research, please cite the paper in the Journal of
Open Source Software:

Steppi A, Gyori BM, Bachman JA (2020). Adeft: Acromine-based Disambiguation of
Entities from Text with applications to the biomedical literature.  *Journal of
Open Source Software,* 5(45), 1708, https://doi.org/10.21105/joss.01708

## Installation

Adeft works with Python versions 3.8 and above. It is available on PyPi and can be installed with the command

$ pip install adeft

Adeft's pretrained machine learning models can then be downloaded with the command

$ python -m adeft.download

If you choose to install by cloning this repository

$ git clone https://github.com/indralab/adeft.git

You should also run

$ python setup.py build_ext --inplace

at the top level of your local repository in order to build the extension module
for alignment based longform detection and scoring.

## Using Adeft
A dictionary of available models can be imported with `from adeft import available_models`

The dictionary maps shortforms to model names. It's possible for multiple equivalent
shortforms to map to the same model.

Here's an example of running a disambiguator for ER on a list of texts

```python
from adeft.disambiguate import load_disambiguator

er_dd = load_disambiguator('ER')

...

er_dd.disambiguate(texts)
```

Users may also build and train their own disambiguators. See the documention
for more info.

## Documentation

Documentation is available at
[https://adeft.readthedocs.io](http://adeft.readthedocs.io)

Jupyter notebooks illustrating Adeft workflows are available under `notebooks`:
- [Introduction](notebooks/introduction.ipynb)
- [Model building](notebooks/model_building.ipynb)

## Testing

Adeft uses `pytest` for unit testing, and uses Github Actions as a
continuous integration environment. To run tests locally, make sure
to install the test-specific requirements listed in setup.py as

```bash
pip install adeft[test]
```

and download all pre-trained models as shown above.
Then run `pytest` in the top-level `adeft` folder.

## Funding

Development of this software was supported by the Defense Advanced Research
Projects Agency under awards W911NF018-1-0124 and W911NF-15-1-0544, and the
National Cancer Institute under award U54-CA225088.

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