This second edition textbook covers a coherently organized
framework for text analytics, which integrates material drawn from
the intersecting topics of information retrieval, machine learning,
and natural language processing. Particular importance is placed on
deep learning methods. The chapters of this book span three broad
categories:1. Basic algorithms: Chapters 1 through 7 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 and information retrieval: 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. Natural language processing: Chapters 10 through 16
discuss various sequence-centric and natural language applications,
such as feature engineering, neural language models, deep learning,
transformers, pre-trained language models, text summarization,
information extraction, knowledge graphs, question answering,
opinion mining, text segmentation, and event detection. Compared to
the first edition, this second edition textbook (which targets
mostly advanced level students majoring in computer science and
math) has substantially more material on deep learning and natural
language processing. Significant focus is placed on topics like
transformers, pre-trained language models, knowledge graphs, and
question answering.
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