Machine Learning for Text

Text analytics is a field that combines information retrieval, machine learning, and natural language processing, and this textbook provides a comprehensive framework covering basic algorithms, domain-sensitive mining, and sequence-centric mining. It is designed for graduate students in computer science and researchers and practitioners in related fields, with a solution manual for classroom teaching.

Format: Hardback
Length: 493 pages
Publication date: 03 April 2018
Publisher: Springer International Publishing AG

Text analytics is a multifaceted field that intersects information retrieval, machine learning, and natural language processing, offering a comprehensive framework for understanding and analyzing textual data. This textbook is meticulously designed to cover this complex domain, presenting a coherent and organized approach drawn from these interrelated areas.

The chapters of this textbook are structured into three distinct categories:

Basic Algorithms: Chapters 1 through 7 delve into the classical algorithms employed in machine learning from text. These algorithms encompass preprocessing techniques, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. These chapters provide a solid foundation for understanding the fundamental methods used to extract meaningful information and insights from text.

Domain-Sensitive Mining: Chapters 8 and 9 explore the learning methods when text is combined with diverse domains such as multimedia and the Web. The problem of information retrieval and Web search is discussed in the context of its relationship with ranking and machine learning methods. These chapters highlight the importance of leveraging text data in specific applications and industries.

Sequence-Centric Mining: Chapters 10 through 14 delve into various sequence-centric and natural language applications. These chapters cover topics such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. These applications demonstrate the practical applications of text analytics in diverse fields, including healthcare, finance, social media, and more.

This textbook offers an in-depth exploration of machine learning topics for text, providing a comprehensive coverage that caters to the needs of graduate students in computer science, researchers, professors, and industrial practitioners working in these related fields. The presentation is primarily text-centric, focusing on the algorithms and techniques applicable to textual data. However, Chapters 3 to 7 also cover machine learning algorithms that have broader applications beyond text data. This versatility makes the book suitable for offering courses in text analytics as well as providing a broader perspective of machine learning with text as a backdrop.

The textbook is designed to cater to a diverse audience, including students who are new to the field of text analytics, as well as those with prior knowledge and experience. It provides detailed explanations, examples, and exercises to reinforce the concepts and facilitate a deeper understanding of the subject matter. Additionally, the textbook includes references to relevant research papers and industry reports, enabling readers to further explore the latest developments and applications in the field.

In conclusion, text analytics is a rapidly evolving field that plays a crucial role in extracting valuable information and insights from textual data. This textbook offers a comprehensive and up-to-date coverage of the domain, providing a solid foundation for students, researchers, and practitioners alike. With its extensive coverage, interdisciplinary approach, and practical applications, this textbook is an invaluable resource for anyone interested in advancing their knowledge and expertise in text analytics.

Weight: 1118g
Dimension: 255 x 191 x 35 (mm)
ISBN-13: 9783319735306
Edition number: 1st ed. 2018