Machine Learning for Text

ISBN-13: 9783030088071 / Angielski / Miękka / 2019 / 493 str.

Charu C. Aggarwal

Opis

Widoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej. Nie gwarantujemy zgodności okładki z prezentowanym zdjęciem.

Recenzje

"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)

Spis treści

1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Text Sequence Modeling and Deep Learning.- 11 Text Summarization.- 12 Information Extraction.- 13 Opinion Mining and Sentiment Analysis.- 14 Text Segmentation and Event Detection.

O autorze

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining. He has published more than 350 papers in refereed conferences and journals and authored over 80 patents. He is the author or editor of 17 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of two IBM Outstanding Technical Achievement Awards (2009, 2015) for his work on data streams/high-dimensional data. He received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He is also a recipient of the IEEE ICDM Research Contributions Award (2015), which is one of the two highest awards for influential research contributions in the field of data mining. He has served as the general co-chair of the IEEE Big Data Conference (2014) and as the program co-chair of the ACM CIKM Conference (2015), the IEEE ICDM Conference (2015), and the ACM KDD Conference (2016). He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the IEEE Transactions on Big Data, an action editor of the Data Mining and Knowledge Discovery Journal, and an associate editor of the Knowledge and Information Systems Journal. He has served as editor-in-chief of the ACM SIGKDD Explorations (2014–2017) and is currently an editor-in-chief of the ACM Transactions on Knowledge Discovery from Data. He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer. He has served as the vice-president of the SIAM Activity Group on Data Mining and is a member of the SIAM industry committee. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”

Blurb

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.