adeft 0.12.3

Description:

adeft 0.12.3

Adeft







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
algorithm developed by the NaCTeM 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 3.5 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
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
Jupyter notebooks illustrating Adeft workflows are available under notebooks:

Introduction
Model building

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
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.