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        "author": "adeft developers, Harvard Medical School",
        "author_email": "albert_steppi@hms.harvard.edu",
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        "classifiers": [
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        "description": "# Adeft\n[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)\n[![Build](https://travis-ci.org/indralab/adeft.svg)](https://travis-ci.org/indralab/adeft)\n[![Documentation](https://readthedocs.org/projects/adeft/badge/?version=latest)](https://adeft.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/adeft.svg)](https://badge.fury.io/py/adeft)\n[![Python 3](https://img.shields.io/pypi/pyversions/adeft.svg)](https://www.python.org/downloads/release/python-357/)\n\nAdeft (Acromine based Disambiguation of Entities From Text context)\nis 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\nby the [NaCTeM](http://www.nactem.ac.uk/index.php) at the University of Manchester\nto identify possible longform expansions for shortforms in a text corpus.\nIt allows users to build disambiguation models to disambiguate shortforms based\non their text context. A growing number of pretrained disambiguation models are publicly available to download through adeft.\n\n## Installation\n\nAdeft works with Python versions 3.5 and above. It is available on PyPi and can be installed with the command\n\n    $ pip install adeft\n\nAdeft's pretrained machine learning models can then be downloaded with the command\n\n    $ python -m adeft.download\n\n## Using Adeft\nA dictionary of available models can be imported with `from adeft import available_models`\n\nThe dictionary maps shortforms to model names. It's possible for multiple equivalent\nshortforms to map to the same model.\n\nHere's an example of running a disambiguator for ER on a list of texts\n\n```python\nfrom adeft.disambiguate import load_disambiguator\n\ner_dd = load_disambiguator('ER')\n\n    ...\n\ner_dd.disambiguate(texts)\n```\n\nUsers may also build and train their own disambiguators. See the documention\nfor more info.\n\n\n## Documentation\n\nDocumentation is available at\n[https://adeft.readthedocs.io](http://adeft.readthedocs.io)\n\nJupyter notebooks illustrating Adeft workflows are available under `notebooks`:\n- [Introduction](notebooks/introduction.ipynb)\n- [Model building](notebooks/model_building.ipynb)\n\n\n## Testing\n\nAdeft uses `nosetests` for unit testing, and is integrated with the Travis\ncontinuous integration environment. To run tests locally, make sure\nto install the test-specific requirements listed in setup.py as\n\n```bash\npip install adeft[test]\n```\n\nand download all pre-trained models as shown above.\nThen run `nosetests` in the top-level `adeft` folder.\n\n## Funding\n\nDevelopment of this software was supported by the Defense Advanced Research\nProjects Agency under award W911NF018-1-0124 and the National Cancer Institute\nunder award U54-CA225088.\n\n\n",
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        "name": "adeft",
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