rake-nltk

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RAKE short for Rapid Automatic Keyword Extraction algorithm, is a domain independent keyword extraction algorithm which tries to determine key phrases in a body of text by analyzing the frequency of word appearance and its co-occurance with other words in the text.

|Demo|

Features

• Ridiculously simple interface.
• Configurable word and sentence tokenizers, language based stop words etc
• Configurable ranking metric.

Setup

Using pip

.. code:: bash

    pip install rake-nltk

Directly from the repository

.. code:: bash

git clone https://github.com/csurfer/rake-nltk.git
python rake-nltk/setup.py install

Quick Start

.. code:: python

from rake_nltk import Rake

# Uses stopwords for english from NLTK, and all puntuation characters by
# default
r = Rake()

# Extraction given the text.
r.extract_keywords_from_text(<text to process>)

# Extraction given the list of strings where each string is a sentence.
r.extract_keywords_from_sentences(<list of sentences>)

# To get keyword phrases ranked highest to lowest.
r.get_ranked_phrases()

# To get keyword phrases ranked highest to lowest with scores.
r.get_ranked_phrases_with_scores()

Debugging Setup

If you see a stopwords error, it means that you do not have the corpus stopwords downloaded from NLTK. You can download it using command below.

.. code:: bash

python -c "import nltk; nltk.download('stopwords')"

References

This is a python implementation of the algorithm as mentioned in paper Automatic keyword extraction from individual documents by Stuart Rose, Dave Engel, Nick Cramer and Wendy Cowley_

Why I chose to implement it myself?

• It is extremely fun to implement algorithms by reading papers. It is the digital equivalent of DIY kits.
• There are some rather popular implementations out there, in python(\ aneesha/RAKE) and node(\ waseem18/node-rake) but neither seemed to use the power of NLTK_. By making NLTK an integral part of the implementation I get the flexibility and power to extend it in other creative ways, if I see fit later, without having to implement everything myself.
• I plan to use it in my other pet projects to come and wanted it to be modular and tunable and this way I have complete control.

Contributing

Bug Reports and Feature Requests

Please use `issue tracker`_ for reporting bugs or feature requests.

Development
~~~~~~~~~~~

1. Checkout the repository.
2. Make your changes and add/update relavent tests.
3. Install **`poetry`** using **`pip install poetry`**.
4. Run **`poetry install`** to create project's virtual environment.
5. Run tests using **`poetry run tox`** (Any python versions which you don't have checked out will fail this). Fix failing tests and repeat.
6. Make documentation changes that are relavant.
7. Install **`pre-commit`** using **`pip install pre-commit`** and run **`pre-commit run --all-files`** to do lint checks.
8. Generate documentation using **`poetry run sphinx-build -b html docs/ docs/_build/html`**.
9. Generate **`requirements.txt`** for automated testing using **`poetry export --dev --without-hashes -f requirements.txt > requirements.txt`**.
10. Commit the changes and raise a pull request.

Buy the developer a cup of coffee!

If you found the utility helpful you can buy me a cup of coffee using

|Donate|

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.. _Automatic keyword extraction from individual documents by Stuart Rose, Dave Engel, Nick Cramer and Wendy Cowley: https://www.researchgate.net/profile/Stuart_Rose/publication/227988510_Automatic_Keyword_Extraction_from_Individual_Documents/links/55071c570cf27e990e04c8bb.pdf .. _aneesha/RAKE: https://github.com/aneesha/RAKE .. _waseem18/node-rake: https://github.com/waseem18/node-rake .. _NLTK: http://www.nltk.org/ .. _issue tracker: https://github.com/csurfer/rake-nltk/issues

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