As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

A huge amount of data is generated daily leading to big data challenges. One of them is
related to text mining, especially text classification. To perform this task we usually need a …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

GPT (Generative Pre-trained Transformer) represents advanced language models that have
significantly reshaped the academic writing landscape. These sophisticated language …

A significant part of natural language processing (NLP) techniques for sentiment analysis is
based on supervised methods, which are affected by the quality of data. Therefore …

In the aftermath of GPT-3.5, commonly known as ChatGPT, research have attempted to
assess its capacity for lowering annotation cost, either by doing zero-shot learning …

Question classification (QC) involves classifying given question based on the expected
answer type and is an important task in the Question Answering (QA) system. Existing …

Gathering cyber threat intelligence from open sources is becoming increasingly important for
maintaining and achieving a high level of security as systems become larger and more …

Obtaining high-quality, diverse, accurate datasets for sentiment analysis has always been a
significant challenge. Traditional approaches include annotators, which may introduce bias …