A systematic review on data scarcity problem in deep learning: solution and applications
Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …
applications. Deep learning models require a large amount of data to train the model. In …
[HTML][HTML] SRL-ACO: A text augmentation framework based on semantic role labeling and ant colony optimization
A Onan - Journal of King Saud University-Computer and …, 2023 - Elsevier
The process of creating high-quality labeled data is crucial for training machine-learning
models, but it can be a time-consuming and labor-intensive process. Moreover, manual …
models, but it can be a time-consuming and labor-intensive process. Moreover, manual …
GTR-GA: Harnessing the power of graph-based neural networks and genetic algorithms for text augmentation
A Onan - Expert systems with applications, 2023 - Elsevier
Text augmentation is a popular technique in natural language processing (NLP) that has
been shown to improve the performance of various downstream tasks. The goal of text …
been shown to improve the performance of various downstream tasks. The goal of text …
Tailored text augmentation for sentiment analysis
In synonym replacement-based data augmentation techniques for natural language
processing tasks, words in a sentence are often sampled randomly with equal probability. In …
processing tasks, words in a sentence are often sampled randomly with equal probability. In …
Chinese engineering geological named entity recognition by fusing multi-features and data enhancement using deep learning
The engineering geology report serves as a comprehensive portrayal of the geological
conditions and information within a surveyed region, making it highly valuable for extracting …
conditions and information within a surveyed region, making it highly valuable for extracting …
Feature-aware conditional GAN for category text generation
Category text generation receives considerable attentions since it is beneficial for various
natural language processing tasks. Recently, the generative adversarial network (GAN) has …
natural language processing tasks. Recently, the generative adversarial network (GAN) has …
Unlock the potential of counterfactually-augmented data in out-of-distribution generalization
Abstract Counterfactually-Augmented Data (CAD)–minimal editing of sentences to flip the
corresponding labels–has the potential to improve the Out-Of-Distribution (OOD) …
corresponding labels–has the potential to improve the Out-Of-Distribution (OOD) …
Boosting text augmentation via hybrid instance filtering framework
Text augmentation is an effective technique for addressing the problem of insufficient data in
natural language processing. However, existing text augmentation methods tend to focus on …
natural language processing. However, existing text augmentation methods tend to focus on …
A Levenshtein distance-based method for word segmentation in corpus augmentation of geoscience texts
For geoscience text, rich domain corpora have become the basis of improving the model
performance in word segmentation. However, the lack of domain-specific corpus with …
performance in word segmentation. However, the lack of domain-specific corpus with …
Data augmentation strategies to improve text classification: a use case in smart cities
L Bencke, VP Moreira - Language Resources and Evaluation, 2024 - Springer
Text classification is a very common and important task in Natural Language Processing. In
many domains and real-world settings, a few labeled instances are the only resource …
many domains and real-world settings, a few labeled instances are the only resource …