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Natural Language Processing (NLP) constitutes a pivotal domain of artificial intelligence focused on enabling computers to comprehend, process, and generate human language. Text classification, a fundamental NLP task, aims to categorize text into predefined classes. In recent years, deep learning has emerged as a dominant force across various research domains and has become a staple technology within NLP, particularly in text classification tasks. Unlike numerical and visual data, text processing underscores the need for nuanced processing capabilities. Traditional text classification methodologies typically involve preprocessing textual data and annotating samples manually to derive effective feature presentations for classification using classical machine learning algorithms. This paper delves into the current landscape of deep learning applications in NLP, specifically in three core areas: text representation, sequence modeling, and knowledge representation. Furthermore, it explores the advancements and synergies facilitated by natural language processing in the realm of text classification, while also addressing challenges posed by adversarial techniques intext generation, classification, and semantic parsing. An empirical investigation into text classification tasks demonstrates the efficacy of interactive integration training, particularly in tandem with TextCNN, underscoring the pivotal role of these advancements in augmenting and refining text classification methodologies.
Xu et al. (Thu,) studied this question.