https://github.com/philgooch/abbreviation-extraction

Python3 implementation of the Schwartz-Hearst algorithm for extracting abbreviation-definition pairs
https://github.com/philgooch/abbreviation-extraction

abbreviations information-extraction keyword-extraction nlp python3

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Python3 implementation of the Schwartz-Hearst algorithm for extracting abbreviation-definition pairs

• Host: GitHub
• URL: https://github.com/philgooch/abbreviation-extraction
• Owner: philgooch
• License: mit
• Created: 2017-10-25T11:09:16.000Z (almost 7 years ago)
• Default Branch: develop
• Last Pushed: 2023-10-20T21:33:57.000Z (11 months ago)
• Last Synced: 2024-04-24T13:10:26.830Z (5 months ago)
• Topics: abbreviations, information-extraction, keyword-extraction, nlp, python3
• Language: Python
• Homepage:
• Size: 57.6 KB
• Stars: 85
• Watchers: 7
• Forks: 19
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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# Extraction of abbreviation-definition pairs

[![Build Status](https://travis-ci.org/philgooch/abbreviation-extraction.svg)](https://travis-ci.org/philgooch/abbreviation-extraction)

## Version: 0.2.5

This is a Python3 implementation of the [Schwartz-Hearst algorithm](https://psb.stanford.edu/psb-online/proceedings/psb03/schwartz.pdf)

for identifying abbreviations and their corresponding definitions in free text[1].

The [original implementation is in Java](http://biotext.berkeley.edu/software.html), and Vincent Van Asch created a Python2 implementation at

http://www.cnts.ua.ac.be/~vincent/scripts/abbreviations.py

* NB: As of March 2019 this link appears to be dead. 

I have simplified, refactored it for Python 3 and added some tests.

This version outputs a Python dictionary of abbreviation:definition pairs.

## Installation for command-line use

    pip install -r requirements.txt

    

### Usage

From the command line

    python abbreviations/schwartz_hearst.py 

    

## Installation as a module

    python3 setup.py install

    

or

    pip install abbreviations

    

### Usage

    from abbreviations import schwartz_hearst

    

    # By default, the most recently encountered definition for each term is returned

    pairs = schwartz_hearst.extract_abbreviation_definition_pairs(doc_text='The emergency room (ER) was busy')

    pairs = schwartz_hearst.extract_abbreviation_definition_pairs(file_path='')

    # If multiple definitions are encountered for each term, you might want to return the most common for each

    pairs = schwartz_hearst.extract_abbreviation_definition_pairs(doc_text='...', most_common_definition=True)

    

    # ... or you might want to return the first encountered definition for each

    pairs = schwartz_hearst.extract_abbreviation_definition_pairs(doc_text='...', first_definition=True)

    

    # when using a longer text, the format is line-separated sentences:

    import nltk

    sentences = nltk.sent_tokenize(longer_text)

    pairs = schwartz_hearst.extract_abbreviation_definition_pairs(doc_text='\n'.join(sentences))

[1] A. Schwartz and M. Hearst (2003) A Simple Algorithm for Identifying Abbreviations Definitions in Biomedical Text.

Biocomputing, 451-462.