Machine Learning for Text (eBook, PDF)
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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 frameworkdrawn 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 analyticssuch 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 heterog...
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.
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.
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