Initial release, 16-07-2019
TextAugment is a Python 3 library for augmenting text for natural language processing applications. TextAugment stands on the giant shoulders of NLTK, Gensim, and TextBlob and plays nicely with them.
Requirements
- Python 3
The following software packages are dependencies and will be installed automatically.
$ pip install numpy nltk gensim textblob googletrans The following code downloads wordnet, tokenizer, and part-of-speech tagger model.
nltk.download('wordnet')
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')Install from pip [Recommended]
$ pip install textaugment
How to use
>>> from textaugment import Word2vec
>>> t = Word2vec(model='path/to/gensim/model'or 'gensim model itself')
>>> t.augment('The stories are good')
The films are goodCitation
@article{marivate2019improving,
title={Improving short text classification through global augmentation methods},
author={Marivate, Vukosi and Sefara, Tshephisho},
journal={arXiv preprint arXiv:1907.03752},
year={2019}
}