Free PDF Machine Learning for Text, by Charu C. Aggarwal

Free PDF Machine Learning for Text, by Charu C. Aggarwal

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Machine Learning for Text, by Charu C. Aggarwal

Free PDF Machine Learning for Text, by Charu C. Aggarwal

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Review

“The book discusses many key technologies used today in social media, such as opinion mining or event detection. One of the most promising new technologies, deep learning, is discussed as well. This book is an excellent resource for programmers and graduate students interested in becoming experts in the text mining field. … Summing Up: Recommended. Graduate students, researchers, and professionals.” (J. Brzezinski, Choice, Vol. 56 (04), December, 2018)

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From the Back Cover

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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Product details

Hardcover: 493 pages

Publisher: Springer; 1st ed. 2018 edition (March 20, 2018)

Language: English

ISBN-10: 3319735306

ISBN-13: 978-3319735306

Product Dimensions:

7 x 1.1 x 10 inches

Shipping Weight: 2.4 pounds (View shipping rates and policies)

Average Customer Review:

3.8 out of 5 stars

4 customer reviews

Amazon Best Sellers Rank:

#217,195 in Books (See Top 100 in Books)

This is an excellent textbook for academia and industry alike, although the style leans towards academia. The book introduces various machine learning methods in detail like matrix factorization, PLSA, LDA, SVD, clustering, classification, and deep learning. The exposition is clear, intuitive, and certainly not dry, which can sometimes be a risk for mathematically oriented books. Coverage of deep learning includes some unique perspectives on word2vec, RNNs, and LSTMs. Beautiful presentation of word2vec is provided together with its relationship to matrix factorization. Even though word2vec is also covered in Jurafsky (Chapter 16) and in some other books, the details here are far greater than any other book I have seen.The insights and connections in the book are pretty amazing. Word2vec is connected to matrix factorization, SVMs/RFs are connected to nearest neighbors, NMF is connected to PLSA, and so on. Insights are provide on why kernels truly work and the procedure for systematic kernelization of arbitrary problems is provided. Other covered topics include opinion mining, summarization, text segmentation, and information extraction. Examples and pseudocodes are given in many chapters.The book covers all the three aspects of machine learning (deep focus), information retrieval, (light focus), and sequence-centric topics like information extraction/summarization. The style and quality of writing is somewhat similar to "An Introduction to Information Retrieval" by Manning although the content and coverage are quite different (and more extensive). Manning focuses on IR and touches on machine learning, whereas this book focuses on machine learning and touches on IR. The book contains about 500 pages, although the amount of content is larger than other books of comparable size because of the smaller font used.This book is clearly not a programming or implementation book, and it seems to be targeted to university classrooms, with a presentation thatis independent of specific programming frameworks (like Python). This is on par for a university textbook, and preferable for someone from academia. Nevertheless, industry can benefit too, because the pseudocodes and detailed explanations are sufficient for a mathematically and algorithmically competent engineer to implement what they want. The author also provides bibliographic summaries with pointers to softwareresources.

Great book with extensive coverage coupled with lots of intuitionand insight. As one reviewer mentions, it is certainly not a programmingbook and is written as a university textbook. The writing is clearand precise, but it is certainly not a programming recipe or cookbook either.I couple this book with other similar books like Manning's books andJurafsky's book to get a full picture of the field. I must say thatthe coverage is heads and shoulders above other books in depth and breadth.How many books discuss IR, general machine learning, deep learning,search engines, and information extraction all at one place?

Provides a detailed treatment of the text machine learning.Will appeal to professors, students and researchers.The book is officially classified as a textbook, and is intendedfor classroom teaching in universities.The nice writing style also makes it accessible to practitioners.A lot of explanations are given in order to explain very difficultmathematical concepts. The book makes a special effort toexplain the reasoning for different methods. The mathematicsand algorithms are described extremely well, and exercisesare available for class room teaching. The number of topicscovered is impressive, beginning from traditional machinelearning with bag of words, and including deep learning/processing of text as sequences. The topic modeling and matrix factorization discussions are presented in a very integrated way, so that the advantages and disadvantages of different methods become very clear. The lucidity of the book isvery high, and the concepts are easy to follow.I strongly recommend the book, especially if you are looking forgaining a better conceptual understanding.

very vague

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Machine Learning for Text, by Charu C. Aggarwal PDF

Machine Learning for Text, by Charu C. Aggarwal PDF

Machine Learning for Text, by Charu C. Aggarwal PDF

Machine Learning for Text, by Charu C. Aggarwal PDF