Machine Learning for Text
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Beschreibung
Text analytics is a field that lies on the interface of information retrieval, machine learning,
and natural language processing. This book carefully covers a coherently organized framework
drawn from these intersecting topics. The chapters of this book span three broad categories:
1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics
such as preprocessing, similarity computation, topic modeling, matrix factorization,
clustering, classification, regression, and ensemble analysis.
2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous
settings such as a combination of text with multimedia or Web links. The problem of
information retrieval and Web search is also discussed in the context of its relationship
with ranking and machine learning methods.
3. 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 book covers text analytics and machine learning topics from the simple to the advanced.
Since the coverage is extensive, multiple courses can be offered from the same book,
depending on course level.
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.
The first textbook to cover machine learning of text in a holistic way, which includes aspects of mining, language modeling, and deep learning Includes many examples to simplify exposition and facilitate in learning. Semantically understandable illustrations are provided, so that they can be used in classroom teaching Provides comprehensive coverage of this field.The depth and breadth of coverage is unique to this textbook Request lecturer material: sn.pub/lecturer-material
Autor*in
Charu C. Aggarwal
Themen in »Machine Learning for Text«
Data mining Text mining Information retrieval Text clustering Dimensionality reduction Matrix factorization Text classification Mining text data text analytics natural language processing machine learning deep learning information extraction opinion mining word2vec
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“The book discusses many key technologies used today in social media, such as opinion mining or event detection. One of the most promising new technologies, deep learning, is discussed as well. This book is an excellent resource for programmers and graduate students interested in becoming experts in the text mining field. … Summing Up: Recommended. Graduate students, researchers, and professionals.” (J. Brzezinski, Choice, Vol. 56 (04), December, 2018)
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