Text Classification: A Perspective of Deep Learning Methods
Zhongwei Wan
In recent years, with the rapid development of information on the Internet,
the number of complex texts and documents has increased exponentially, which
requires a deeper understanding of deep learning methods in order to accurately
classify texts using deep learning techniques, and thus deep learning methods
have become increasingly important in text classification. Text classification
is a class of tasks that automatically classifies a set of documents into
multiple predefined categories based on their content and subject matter. Thus,
the main goal of text classification is to enable users to extract information
from textual resources and process processes such as retrieval, classification,
and machine learning techniques together in order to classify different
categories. Many new techniques of deep learning have already achieved
excellent results in natural language processing. The success of these learning
algorithms relies on their ability to understand complex models and non-linear
relationships in data. However, finding the right structure, architecture, and
techniques for text classification is a challenge for researchers. This paper
introduces deep learning-based text classification algorithms, including
important steps required for text classification tasks such as feature
extraction, feature reduction, and evaluation strategies and methods. At the
end of the article, different deep learning text classification methods are
compared and summarized.