Acromine based Disambiguation of Entities From Text
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.
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
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.
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 is available at https://adeft.readthedocs.io
Jupyter notebooks illustrating Adeft workflows are available under
nosetests for unit testing, and is integrated with the Travis
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.
nosetests in the top-level
Development of this software was supported by the Defense Advanced Research Projects Agency under award W911NF018-1-0124 and the National Cancer Institute under award U54-CA225088.
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