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Python Keyphrase Extraction module
| Date | Stars |
|---|---|
| 2026-07-24 | 1589 |
| 2026-07-25 | 1589 |
| 2026-07-28 | 1589 |
| 2026-07-30 | 1589 |
| 2026-08-06 | 1589 |
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# `pke` - python keyphrase extraction
`pke` is an **open source** python-based **keyphrase extraction** toolkit. It
provides an end-to-end keyphrase extraction pipeline in which each component can
be easily modified or extended to develop new models. `pke` also allows for
easy benchmarking of state-of-the-art keyphrase extraction models, and
ships with supervised models trained on the
[SemEval-2010 dataset](http://aclweb.org/anthology/S10-1004).
## Table of Contents
* [Installation](#installation)
* [Minimal example](#minimal-example)
* [Getting started](#getting-started)
* [Implemented models](#implemented-models)
* [Model performances](#model-performances)
* [Citing pke](#citing-pke)
## Installation
To pip install `pke` from github:
```bash
pip install git+https://github.com/boudinfl/pke.git
```
`pke` relies on `spacy` (>= 3.2.3) for text processing and requires [models](https://spacy.io/usage/models) to be installed:
```bash
# download the english model
python -m spacy download en_core_web_sm
```
## Minimal example
`pke` provides a standardized API for extracting keyphrases from a document.
Start by typing the 5 lines below. For using another model, simply replace
`pke.unsupervised.TopicRank` with another model ([list of implemented models](#implemented-models)).
```python
import pke
# initialize keyphrase extraction model, here TopicRank
extractor = pke.unsupervised.TopicRank()
# load the content of the document, here document is expected to be a simple
# test string and preprocessing is carried out using spacy
extractor.load_document(input='text', language='en')
# keyphrase candidate selection, in the case of TopicRank: sequences of nouns
# and adjectives (i.e. `(Noun|Adj)*`)
extractor.candidate_selection()
# candidate weighting, in the case of TopicRank: using a random walk algorithm
extractor.candidate_weighting()
# N-best selection, keyphrases contains the 10 highest scored candidates as
# (keyphrase, score) tuples
keyphrases = extractor.get_n_best(n=10)
```
A detailed example is provided in the [`examples/`](examples/) directory.
## Getting started
To get your hands dirty with `pke`, we invite you to try our tutorials out.
| Name | Link |
| ---------------------------------------------- | ---------- |
| Getting started with `pke` and keyphrase extraction | [](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-1-graph-based-keyphrase-extraction.ipynb) |
| Model parameterization | [](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-2-parameterization.ipynb) |
| Benchmarking models | [](https://colab.research.google.com/github/keyphrasification/hands-on-with-pke/blob/main/part-3-benchmarking-models.ipynb) |
## Implemented models
`pke` currently implements the following keyphrase extraction models:
* Unsupervised models
* Statistical models
* FirstPhrases
* TfIdf
* KPMiner [(El-Beltagy and Rafea, 2010)](http://www.aclweb.org/anthology/S10-1041.pdf)
* YAKE [(Campos et al., 2020)](https://doi.org/10.1016/j.ins.2019.09.013)
* Graph-based models
* TextRank [(Mihalcea and Tarau, 2004)](http://www.aclweb.org/anthology/W04-3252.pdf)
* SingleRank [(Wan and Xiao, 2008)](http://www.aclweb.org/anthology/C08-1122.pdf)
* TopicRank [(Bougouin et al., 2013)](http://aclweb.org/anthology/I13-1062.pdf)
* TopicalPageRank [(Sterckx et al., 2015)](http://users.intec.ugent.be/cdvelder/papers/2015/sterckx2015wwwb.pdf)
* PositionRank [(Florescu and Caragea, 2017)](httExcerpt of 5,264 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6a2fe93e065c1ff9, topic:natural-language-processing