Metadata-Version: 2.1
Name: adeft
Version: 0.2.1
Summary: Acromine based Disambiguation of Entities From Text context
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/0.2.1.tar.gz
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](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 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
        
        ```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)
            
        
        
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
Description-Content-Type: text/markdown
Provides-Extra: test
