Machine Learning for Text

Machine Learning for Text, Springer, March 2018

Charu C. Aggarwal.

Comprehensive textbook on text mining: Table of Contents

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This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning. The chapters of this book can be organized into three categories:

Classical machine learning methods: These chapters discuss the classical machine learning methods such as matrix factorization, topic modeling, dimensionality reduction, clustering, classification, linear models, and evaluation. All these techniques treat text as a bag of words. Contextual learning methods that combine different types of text and also combine text with heterogeneous data types are covered.

Classical information retrieval and search engines: Although this book is focussed on text mining, the importance of retrieval and ranking methods in mining applications is quite significant. Therefore, the book covers the key aspects of information retrieval, such as data structures, Web ranking, crawling, and search engine design. Importance is given to different types of information retrieval scoring models and learning-to-rank techniques.

Sequence-centric, deep learning, and linguistic methods for mining: While the bag-of-words representation can be useful for traditional applications like classification and clustering, more advanced applications like machine translation, image captioning, opinion mining, information extraction, and text segmentation require one to treat text as a sequence. These chapters discuss methods for sequence-centric mining methods such as deep learning techniques, word2vec, recurrent neural networks, LSTMs, maximum entropy Markov models, and Conditional Random Fields. Custom methods for applications like text summarization, opinion mining, and event detection are also discussed.

The book can be used as a textbook and it contains numerous exercises. However, it is also designed to be useful to researchers and industrial practitioners. It therefore contains extensive bibliographic references for researchers, and the bibliographic section also contains software references for practitioners. Numerous examples and exercises have been provided.

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About the Author

Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.Tech. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining, with particular interests in data streams, privacy, uncertain data and social network analysis. He has published 17 (6 authored and 11 edited) books, over 350 papers in refereed venues, and has applied for or been granted over 80 patents. His h-index is 91. Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and 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 two IBM Outstanding Technical Achievement Awards for his work on streaming systems and high-dimensional data analysis. He has received two best paper awards and an EDBT Test-of-Time Award (2014). He has received the IEEE ICDM Research Contributions Award (2015), which is one of two highest awards for research in the field of data mining. He has served as the general or program co-chair of the IEEE Big Data Conference (2014), the ICDM Conference (2015), the ACM CIKM Conference (2015), and the KDD Conference (2016). He also co-chaired the data mining track at the WWW Conference 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an editor-in-chief of the ACM Transactions on Knowledge Discovery and Data Mining Journal , an action editor of the Data Mining and Knowledge Discovery Journal , an associate editor of the IEEE Transactions on Big Data, and an associate editor of the Knowledge and Information Systems Journal. He is editor-in-chief of the ACM SIGKDD Explorations. He is a fellow of the SIAM (2015), ACM (2013) and the IEEE (2010) for "contributions to knowledge discovery and data mining techniques."

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