Machine learning for text

By:

Aggarwal, Charu C

Material type: Text

TextPublication details: Springer 2018 SwitzerlandDescription: xxiii, 493 pISBN:

9783030088071

Subject(s):

DDC classification:

006.31 AGG

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.

List(s) this item appears in: IT & Decision Sciences | Operation & quantitative Techniques

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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	Checked out	04/07/2025	001690

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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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