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https://github.com/AlirezaTheH/perke

A keyphrase extractor for Persian
https://github.com/AlirezaTheH/perke

data-mining data-processing information-retrieval keyphrase keyphrase-extraction keyphrase-extractor keyword keyword-extraction keyword-extractor machine-learning ml natural-language-processing nlp persian persian-language python text-mining text-processing unsupervised-learning

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A keyphrase extractor for Persian

Host: GitHub
URL: https://github.com/AlirezaTheH/perke
Owner: AlirezaTheH
License: mit
Created: 2020-02-03T14:00:01.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2024-04-29T20:05:32.000Z (5 months ago)
Last Synced: 2024-04-29T21:56:53.800Z (5 months ago)
Topics: data-mining, data-processing, information-retrieval, keyphrase, keyphrase-extraction, keyphrase-extractor, keyword, keyword-extraction, keyword-extractor, machine-learning, ml, natural-language-processing, nlp, persian, persian-language, python, text-mining, text-processing, unsupervised-learning
Language: Python
Homepage:
Size: 141 KB
Stars: 68
Watchers: 1
Forks: 7
Open Issues: 2
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

        # Perke

[![tests](https://github.com/alirezatheh/perke/workflows/tests/badge.svg)](https://github.com/alirezatheh/perke/actions/workflows/tests.yaml)

[![pre-commit.ci](https://results.pre-commit.ci/badge/github/AlirezaTheH/perke/main.svg)](https://results.pre-commit.ci/latest/github/alirezatheh/perke/main)

[![PyPI Version](https://img.shields.io/pypi/v/perke)](https://pypi.python.org/pypi/perke)

[![Python Versions](https://img.shields.io/pypi/pyversions/perke)](https://pypi.org/project/perke)

[![Documentation Status](https://readthedocs.org/projects/perke/badge/?version=stable)](https://perke.readthedocs.io/en/stable/?badge=stable)

Perke is a Python keyphrase extraction package for Persian language. It

provides an end-to-end keyphrase extraction pipeline in which each component

can be easily modified or extended to develop new models.

## Installation

- The easiest way to install is from PyPI:

  ```bash

  pip install perke

  ```

  Alternatively, you can install directly from GitHub:

  ```bash

  pip install git+https://github.com/alirezatheh/perke.git

  ```

- Perke also requires a trained POS tagger model. We use

  [Hazm's](https://github.com/roshan-research/hazm) POS tagger model. You can

  easily download latest [Hazm's](https://github.com/roshan-research/hazm) POS

  tagger using the following command:

  ```bash

  python -m perke download

  ```

  Alternatively, you can use another model with same tag names and structure,

  and put it in the

  [`resources`](https://github.com/alirezatheh/perke/tree/main/perke/resources)

  directory.

## Simple Example

Perke provides a standardized API for extracting keyphrases from a text. Start

by typing the 4 lines below to use `TextRank` keyphrase extractor.

```python

from perke.unsupervised.graph_based import TextRank

# 1. Create a TextRank extractor.

extractor = TextRank()

# 2. Load the text.

extractor.load_text(input='text or path/to/input_file')

# 3. Build the graph representation of the text and weight the

#    words. Keyphrase candidates are composed of the 33 percent

#    highest weighted words.

extractor.weight_candidates(top_t_percent=0.33)

# 4. Get the 10 highest weighted candidates as keyphrases.

keyphrases = extractor.get_n_best(n=10)

```

For more in depth examples see the

[`examples`](https://github.com/alirezatheh/perke/tree/main/examples)

directory.

## Documentation

Documentation and references are available at

[Read The Docs](https://perke.readthedocs.io).

## Implemented Models

Perke currently, implements the following keyphrase extraction models:

- Unsupervised models

    - Graph-based models

        - TextRank: [article](http://www.aclweb.org/anthology/W04-3252.pdf)

          by Mihalcea and Tarau, 2004

        - SingleRank: [article](https://www.aaai.org/Papers/AAAI/2008/AAAI08-136.pdf)

          by Wan and Xiao, 2008

        - TopicRank: [article](http://aclweb.org/anthology/I13-1062.pdf)

          by Bougouin, Boudin and Daille, 2013

        - PositionRank: [article](http://www.aclweb.org/anthology/P17-1102.pdf)

          by Florescu and Caragea, 2017

        - MultipartiteRank: [article](https://www.aclweb.org/anthology/N18-2105.pdf)

          by Boudin, 2018

## Acknowledgements

Perke is inspired by [pke](https://github.com/boudinfl/pke).