kleis-keyphrase-extraction 0.1.1.dev1

Python package for keyphrase labeling.

Kleis: Python package for keyphrase extraction

Kleis is a python package to label keyphrases in scientific text. It is named after the ancient greek word κλείς.

Install

Pip (Easy and quick)

$ pip install kleis-keyphrase-extraction

Make your own wheel

$ git clone https://github.com/sdhdez/kleis-keyphrase-extraction.git
$ cd kleis-keyphrase-extraction/
$ python setup.py sdist bdist_wheel
$ pip install dist/kleis_keyphrase_extraction-0.1.X.devX-py3-none-any.whl

Replace X with the corresponding values.

Note: This method doesn't include pre-trained models, you should download the corpus so it can train.

Usage

Example here

Datasets

Thepackage already includes some pre-trained models but if you want to test by your own you should download the datasets.

Download from SemEval 2017 Task 10 and decompress in "~/kleis_data/corpus/semeval2017-task10" or "./kleis_data/corpus/semeval2017-task10"

$ ls ~/kleis_data/corpus/semeval2017-task10

brat_config  eval.py       __MACOSX            README_data.md  scienceie2017_test_unlabelled  train2   xml_utils.py
dev          eval_py27.py  README_data_dev.md  README.md       semeval_articles_test          util.py  zips

Test

You can test your installation with keyphrase-extraction-example.py

$ python keyphrase-extraction-example.py

Also, see here for another example.

Requirements

• Python 3 (Tested: 3.6.5)
• nltk (with corpus) (Tested: 3.2.5)
• python-crfsuite (Tested: 0.9.5)

Optional

Notebooks

To run the noteooks in this repository install JupyterLab.

$ pip install jupyterlab

Then run the following command.

jupyter lab

Further information

This method uses a CRFs model (Conditional Random Fields) to label keyphrases in text, the model is trained with keyphrase candidates filtered with Part-of-Spech tag sequences. It is based on the method described here, but with a better performance. Please, feel free to send us comments or questions.

In this version we use python-crfsuite.