Machine Learning for Text

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:

- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.

- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 

- Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection.

 This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop).

 This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.

文字分析是一個位於資訊檢索、機器學習和自然語言處理交界的領域，這本教科書詳細介紹了從這些相互關聯的主題中提煉出的一個有條理組織的框架。這本教科書的章節分為三個類別：

- 基本算法：第1章到第7章討論了文本機器學習的經典算法，如預處理、相似度計算、主題建模、矩陣分解、聚類、分類、回歸和集成分析。

- 領域敏感挖掘：第8章和第9章討論了結合多個領域（如多媒體和網絡）的文本學習方法。還討論了資訊檢索和網絡搜索問題，以及它們與排名和機器學習方法的關係。

- 序列中心挖掘：第10章到第14章討論了各種以序列和自然語言為中心的應用，如特徵工程、神經語言模型、深度學習、文本摘要、信息提取、意見挖掘、文本分割和事件檢測。

這本教科書詳細介紹了文本機器學習的主題。由於內容廣泛，可以根據課程水平提供多個課程。儘管呈現方式以文本為中心，但第3章到第7章介紹的機器學習算法通常也應用於文本數據以外的領域。因此，這本書不僅可以用於文本分析課程，還可以從更廣泛的機器學習（以文本為背景）的角度提供課程。

這本教科書針對計算機科學研究生、研究人員、教授和工業從業人員。這本教科書附帶有一本供課堂教學使用的解答手冊。