Text augmentation
Text augmentation is the creation of altered or generated textual examples to improve model robustness or address limited training data.
Deep Learning with R, Third Edition
Text augmentation is the creation of altered or generated textual training examples intended to improve a model's robustness or address data scarcity.
- How it is used
- Uses altered or generated text examples to improve model robustness when training data is limited or overfitting appears.
- How deep it goes
- Explains deletion, swapping, and synonym replacement with examples, then emphasizes semantic risks and practical limitations.
- Examples
- Uses random word deletion, word swapping, synonym replacement, and a sentence about rain in Spain.
- What you can do after
- Apply basic text edits while evaluating whether they preserve meaning and improve robustness.
Read how Deep Learning with R, Third Edition explains text augmentation →
Deep Learning with Python, Third Edition
A family of methods that create modified text examples to improve model robustness.
- How it is used
- Uses modified text inputs to improve robustness when limited training data encourages overfitting.
- How deep it goes
- Defines basic edits, demonstrates their risks, and stops short of recommending text augmentation as a widespread practice.
- Examples
- Uses random word deletion, word swapping, and synonym replacement, including a sentence whose word order is deliberately distorted.
- What you can do after
- Evaluate whether simple text edits improve robustness without changing an example’s intended meaning.
Read how Deep Learning with Python, Third Edition explains text augmentation →
Related concepts
- data augmentationkind of · Data augmentation is the process of adding or deriving examples, variables, or associated information from existing data to increase its variety or support subsequent analysis or computation.
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