Using traditional machine learning approaches, there is no single feature engineering
solution for all text mining and learning tasks. Thus, researchers must determine and …

Various studies have demonstrated that convolutional neural networks (CNNs) can be
directly applied to different levels of text embedding, such as character‐, word‐, or document …

This article offers an empirical exploration on the use of character-level convolutional
networks (ConvNets) for text classification. We constructed several large-scale datasets to …

Text classification is a foundational task in many NLP applications. Traditional text classifiers
often rely on many human-designed features, such as dictionaries, knowledge bases and …

Traditional machine learning requires data to be described by attributes prior to applying a
learning algorithm. In text classification tasks, many feature engineering methodologies …

Text classification is an important task in natural language processing with wide
applications. Traditional text classification methods manually extract the features which are …

User feedback is essential for understanding user needs. In this paper, we use free-text
obtained from a survey on sleep-related issues to build a deep neural networks-based text …

Convolutional neural network (CNN) is a neural network that can make use of the internal
structure of data such as the 2D structure of image data. This paper studies CNN on text …

Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing
architectures used in text classification. Traditional approaches combine the strengths of …

This paper proposes a low-complexity word-level deep convolutional neural network (CNN)
architecture for text categorization that can efficiently represent long-range associations in …