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（TC）在自然语言处理（NLP）领域中占据着核心地位，尤其是在计算机科学和工程的视角下。过去十年中，深度学习彻底改变了TC，推动了文本检索、分类、信息提取和摘要等方面的进步。学术文献中充满了数据集、模型和评估标准，尽管研究涉及阿拉伯语、汉语、印地语等其他语言，但英语仍然是主要关注的语言。TC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TC流程进行全面考察。本专著深入探讨了TC流程，特别强调评估每个组件对TC模型整体性能的影响。流程包括最先进的数据集、文本预处理技术、文本表示方法、分类模型、评估指标、当前结果和未来趋势。每一章都细致地考察了这些阶段，展示了技术创新和重要的最新发现。这项工作批判性地评估了各种分类策略，提供了比较分析、示例、案例研究和实验评估。这些贡献超越了一般的综述，提供了对TC的详细而有见地的探索。 显示英文摘要

摘要： Text Classification (TC) stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through the lens of computer science and engineering. The past decade has seen deep learning revolutionize TC, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature is rich with datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of TC models relies heavily on their ability to capture intricate textual relationships and nonlinear correlations, necessitating a comprehensive examination of the entire TC pipeline. This monograph provides an in-depth exploration of the TC pipeline, with a particular emphasis on evaluating the impact of each component on the overall performance of TC models. The pipeline includes state-of-the-art datasets, text preprocessing techniques, text representation methods, classification models, evaluation metrics, current results and future trends. Each chapter meticulously examines these stages, presenting technical innovations and significant recent findings. The work critically assesses various classification strategies, offering comparative analyses, examples, case studies, and experimental evaluations. These contributions extend beyond a typical survey, providing a detailed and insightful exploration of TC.