Acromine based Disambiguation of Entities From Text context
Project description
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 publically available to download through adeft.
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
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
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