Machine Learning for Text

by Charu C. Aggarwal

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 - Basic 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 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 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.

516 pages, Hardcover

Published April 3, 2018

Reviews

[A Mig · Rating 5 out of 5]

Where to start to learn about text classification and clustering. Provides practical guidance, with some nice toy examples to illustrate some of the algorithms' intricacies. The software resources, along with the bibliographical notes, were quite useful. As a plus, the quotes at the start of each chapter were spot-on.

[Aryan kamboj · Rating 5 out of 5]

Being an avid reader of Charu C. Aggarwal's books, I had to read this book. I was working on a project on Natural Language Processing when I got a chance to read this book. Needless to say, it is a classic. Unlike many other books written on the topic, Aggarwal's book stands out. The thorough explanation of even the smallest topic and "why?" of the various algorithms was very helpful, instead of just throwing the topics on the reader, the author took to things one by one, explaining the reason for why are we doing this and why we need improvement. I have read many articles and blogs on LDA and PLSA but never understood the sole reason behind the development of both. The author clearly explained why it was necessary and also made sure that the mathematical and theoretical balance remains. I managed to read the whole book in a couple of sittings. Most of the times, authors do not consider it important to include Information Retrieval as a part of Natural Language Processing, but Mr. Aggarwal included a whole chapter on Retrieval Methods. These are just a few parts of the book, in all, the whole book is an ocean of information and I would definitely recommend this book to anyone who wants to completely understand NLP and its latest advancements.