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.
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
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
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