TextAugment: Text Augmentation Library
now supports gensim >= 4
now support fasttext models
enhanced code to allow user to select top n words (synonyms/most similar words)
added punctuation insertion
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.
$ 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')
$ pip install textaugment
>>> from textaugment import Word2vec
>>> t = Word2vec(model='path/to/gensim/model'or 'gensim model itself')
>>> t.augment('The stories are good')
The films are good
@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}
}