Machine learning for text
Material type: TextPublication details: 2018 Springer Switzerland Description: xxiii, 493 pISBN: 9783030088071Subject(s): Machine learning | Text processing (Computer science) | Artificial intelligence | Computer science | Data miningDDC classification: 006.31 Summary: Introduction 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.Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
Book | Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 006.31 AGG (Browse shelf(Opens below)) | 1 | Available | 001690 |
Introduction
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.
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