Metadata-Version: 2.1
Name: adeft
Version: 0.11.0
Summary: Acromine based Disambiguation of Entities From Text
Home-page: https://github.com/indralab/adeft
Author: adeft developers, Harvard Medical School
Author-email: albert_steppi@hms.harvard.edu
License: UNKNOWN
Download-URL: https://github.com/indralab/adeft/archive/0.11.0.tar.gz
Description: # Adeft
        [![DOI](https://joss.theoj.org/papers/10.21105/joss.01708/status.svg)](https://doi.org/10.21105/joss.01708)
        [![DOI](https://zenodo.org/badge/156276061.svg)](https://zenodo.org/badge/latestdoi/156276061)
        [![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
        [![Tests](https://github.com/indralab/adeft/actions/workflows/tests.yml/badge.svg)](https://github.com/indralab/adeft/actions/workflows/tests.yml)
        [![Documentation](https://readthedocs.org/projects/adeft/badge/?version=latest)](https://adeft.readthedocs.io/en/latest/?badge=latest)
        [![PyPI version](https://badge.fury.io/py/adeft.svg)](https://badge.fury.io/py/adeft)
        [![Python 3](https://img.shields.io/pypi/pyversions/adeft.svg)](https://www.python.org/downloads/release/python-357/)
        
        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](http://www.chokkan.org/research/acromine/)
        algorithm developed by the [NaCTeM](http://www.nactem.ac.uk/index.php) 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
        
        ```python
        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](http://adeft.readthedocs.io)
        
        Jupyter notebooks illustrating Adeft workflows are available under `notebooks`:
        - [Introduction](notebooks/introduction.ipynb)
        - [Model building](notebooks/model_building.ipynb)
        
        
        ## 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
        
        ```bash
        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 award W911NF018-1-0124 and the National Cancer Institute
        under award U54-CA225088.
        
Keywords: nlp,biology,disambiguation
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: test
