Jul 19, 2021 · Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision.

Apr 21, 2025 · Text augmentation is an important aspect of NLP to generate an artificial corpus. This helps in improving the NLP-based models to generalize better over a lot of different sub-tasks like intent classification, machine translation, chatbot training, image summarization, etc. Text augmentation is used when:

Jun 6, 2021 · This article will show how to code in PyTorch, data augmentation techniques for deep learning problems such as text classification, text generation, etc.

May 14, 2021 · An interesting opportunity for text data augmentation is to construct graph-struc-tured representations of text data. This includes relation and entity encodings in knowledge graphs, grammatical structures in syntax trees, or metadata grounding language data, such as citation networks.

Sep 6, 2024 · Text augmentation techniques like synonym replacement and random insertion are powerful methods to expand and enrich Natural Language Processing (NLP) datasets. These techniques involve modifying text data in various ways to generate new, diverse data points from existing samples.

We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data.

This paper introduces nlpaug, a Python library that provides a wide range of text data augmentation techniques. nlpaug offers functionalities for tasks such as synonym replacement, word insertion, word deletion, character-level modifications, and sentence shuffling.

We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data.

A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset.

An interesting opportunity for text data augmentation is to construct graph-structured representations of text data. This includes relation and entity encodings in knowledge graphs, grammatical structures in syntax trees, or metadata grounding language data, such as …