TextAugment: Improving Short Text Classification through Global Augmentation Methods

You have just found TextAugment.

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.

Table of Contents

• Features
• Citation Paper
  • Requirements
  • Installation
  • How to use
    • Word2vec-based augmentation
    • WordNet-based augmentation
    • RTT-based augmentation
• Easy data augmentation (EDA)
• Mixup augmentation
  • Implementation
• Acknowledgements

Features

• Generate synthetic data for improving model performance without manual effort
• Simple, lightweight, easy-to-use library.
• Plug and play to any machine learning frameworks (e.g. PyTorch, TensorFlow, Scikit-learn)
• Support textual data

Citation Paper

Improving short text classification through global augmentation methods.

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 NLTK corpus for wordnet.

nltk.download('wordnet')

The following code downloads NLTK tokenizer. This tokenizer divides a text into a list of sentences by using an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences.

nltk.download('punkt')

The following code downloads default NLTK part-of-speech tagger model. A part-of-speech tagger processes a sequence of words, and attaches a part of speech tag to each word.

nltk.download('averaged_perceptron_tagger')

Use gensim to load a pre-trained word2vec model. Like Google News from Google drive.

import gensim
model = gensim.models.Word2Vec.load_word2vec_format('./GoogleNews-vectors-negative300.bin', binary=True)

You can also use gensim to load Facebook's Fasttext English and Multilingual models

import gensim
model = gensim.models.fasttext.load_facebook_model('./cc.en.300.bin.gz')

Or training one from scratch using your data or the following public dataset:

• Text8 Wiki

• Dataset from "One Billion Word Language Modeling Benchmark"

Installation

Install from pip [Recommended]

$ pip install textaugment
or install latest release
$ pip install [email protected]:dsfsi/textaugment.git

Install from source

$ git clone [email protected]:dsfsi/textaugment.git
$ cd textaugment
$ python setup.py install

How to use

There are three types of augmentations which can be used:

• word2vec

from textaugment import Word2vec

• wordnet

from textaugment import Wordnet

• translate (This will require internet access)

from textaugment import Translate

Word2vec-based augmentation

See this notebook for an example

Basic example

>>> 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

Advanced example

>>> runs = 1 # By default.
>>> v = False # verbose mode to replace all the words. If enabled runs is not effective. Used in this paper (https://www.cs.cmu.edu/~diyiy/docs/emnlp_wang_2015.pdf)
>>> p = 0.5 # The probability of success of an individual trial. (0.1<p<1.0), default is 0.5. Used by Geometric distribution to selects words from a sentence.

>>> t = Word2vec(model='path/to/gensim/model'or'gensim model itself', runs=5, v=False, p=0.5)
>>> t.augment('The stories are good')
The movies are excellent

WordNet-based augmentation

Basic example

>>> import nltk
>>> nltk.download('punkt')
>>> nltk.download('wordnet')
>>> from textaugment import Wordnet
>>> t = Wordnet()
>>> t.augment('In the afternoon, John is going to town')
In the afternoon, John is walking to town

Advanced example

>>> v = True # enable verbs augmentation. By default is True.
>>> n = False # enable nouns augmentation. By default is False.
>>> runs = 1 # number of times to augment a sentence. By default is 1.
>>> p = 0.5 # The probability of success of an individual trial. (0.1<p<1.0), default is 0.5. Used by Geometric distribution to selects words from a sentence.

>>> t = Wordnet(v=False ,n=True, p=0.5)
>>> t.augment('In the afternoon, John is going to town')
In the afternoon, Joseph is going to town.

RTT-based augmentation

Example

>>> src = "en" # source language of the sentence
>>> to = "fr" # target language
>>> from textaugment import Translate
>>> t = Translate(src="en", to="fr")
>>> t.augment('In the afternoon, John is going to town')
In the afternoon John goes to town

EDA: Easy data augmentation techniques for boosting performance on text classification tasks

This is the implementation of EDA by Jason Wei and Kai Zou.

https://www.aclweb.org/anthology/D19-1670.pdf

See this notebook for an example

Synonym Replacement

Randomly choose n words from the sentence that are not stop words. Replace each of these words with one of its synonyms chosen at random.

Basic example

>>> from textaugment import EDA
>>> t = EDA()
>>> t.synonym_replacement("John is going to town")
John is give out to town

Random Deletion

Randomly remove each word in the sentence with probability p.

Basic example

>>> from textaugment import EDA
>>> t = EDA()
>>> t.random_deletion("John is going to town", p=0.2)
is going to town

Random Swap

Randomly choose two words in the sentence and swap their positions. Do this n times.

Basic example

>>> from textaugment import EDA
>>> t = EDA()
>>> t.random_swap("John is going to town")
John town going to is

Random Insertion

Find a random synonym of a random word in the sentence that is not a stop word. Insert that synonym into a random position in the sentence. Do this n times

Basic example

>>> from textaugment import EDA
>>> t = EDA()
>>> t.random_insertion("John is going to town")
John is going to make up town

Mixup augmentation

This is the implementation of mixup augmentation by Hongyi Zhang, Moustapha Cisse, Yann Dauphin, David Lopez-Paz adapted to NLP.

Used in Augmenting Data with Mixup for Sentence Classification: An Empirical Study.

Mixup is a generic and straightforward data augmentation principle. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularises the neural network to favour simple linear behaviour in-between training examples.

Implementation

See this notebook for an example

Built with ❤ on

• Python

Authors

• Joseph Sefara (http://www.speechtech.co.za)
• Vukosi Marivate (http://www.vima.co.za)

Acknowledgements

Cite this paper when using this library. Arxiv Version

@inproceedings{marivate2020improving,
  title={Improving short text classification through global augmentation methods},
  author={Marivate, Vukosi and Sefara, Tshephisho},
  booktitle={International Cross-Domain Conference for Machine Learning and Knowledge Extraction},
  pages={385--399},
  year={2020},
  organization={Springer}
}

Licence

MIT licensed. See the bundled LICENCE file for more details.