How can I use Python for text data augmentation in machine learning?

How can I use Python for text data augmentation in machine learning?

You can use libraries like NLTK, TextAugment, or Augmentor to perform text data augmentation in Python.

What are some common techniques for text augmentation?

Common techniques include synonym replacement, random insertion, random swap, and back-translation.

Can you show me a code example for synonym replacement?

Sure! Here's a simple example using NLTK:

import nltk
from nltk.corpus import wordnet

def synonym_replacement(text):
    words = text.split()
    for i, word in enumerate(words):
        synonyms = wordnet.synsets(word)
        if synonyms:
            words[i] = synonyms[0].lemmas()[0].name()  # Replace with the first synonym
    return ' '.join(words)

text = 'The quick brown fox jumps over the lazy dog.'
augmented_text = synonym_replacement(text)
print(augmented_text)

What libraries do you recommend for more advanced techniques?

For advanced techniques, I recommend using libraries like TextAttack, nlpaug, or the Hugging Face Transformers library.