标题： 文本分类流程：从浅层开始逐步深入 显示英文标题

标题： The Text Classification Pipeline: Starting Shallow going Deeper

摘要： 文本分类在自然语言处理（NLP）领域中占据核心地位，尤其是在计算机科学和工程视角下。过去十年中，深度学习彻底改变了文本分类，推动了文本检索、分类、信息提取和摘要等方面的进步。学术文献包括数据集、模型和评估标准，尽管有研究涉及阿拉伯语、汉语、印地语等，但英语仍是主要关注语言。文本分类模型的有效性很大程度上依赖于其捕捉复杂文本关系和非线性相关性的能力，这需要对整个文本分类流程进行全面考察。在NLP领域，出现了大量文本表示技术和模型架构，其中大型语言模型（LLMs）和生成式预训练变换器（GPTs）处于领先地位。这些模型能够将大量文本数据转换为包含语义信息的有意义向量表示。文本分类的多学科性质，涵盖数据挖掘、语言学和信息检索，突显了协作研究对该领域发展的重要性。本工作结合了传统和现代的文本挖掘方法，促进了对文本分类的全面理解。 显示英文摘要

摘要： Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature includes datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of text classification models relies heavily on their ability to capture intricate textual relationships and non-linear correlations, necessitating a comprehensive examination of the entire text classification pipeline. In the NLP domain, a plethora of text representation techniques and model architectures have emerged, with Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) at the forefront. These models are adept at transforming extensive textual data into meaningful vector representations encapsulating semantic information. The multidisciplinary nature of text classification, encompassing data mining, linguistics, and information retrieval, highlights the importance of collaborative research to advance the field. This work integrates traditional and contemporary text mining methodologies, fostering a holistic understanding of text classification.