Machine Learning for Text by Charu C Aggarwal
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.
At IBM T.J. Watson Research Center, Charu C.Aggarwal is a Distinguished Research Staff Member (DRSM). Yorktown Heights, New York's J.Watson Research Center In 1993, he earned his bachelor's degree in computer science and his doctorate in computer science from the Indian Institute of Technology in Kanpur. In 1996, he graduated from the Massachusetts Institute of Technology. He's done a lot of work in the field of data mining.
He has authored over 80 patents and has over 400 papers published in professional conferences and publications. He has written or edited 19 books, including data mining, recommender systems, and outlier analysis textbooks. He has been named a Master Inventor at IBM three times, owing to the commercial importance of his innovations. He won an IBM Corporate Award in 2003 for his work on bio-terrorist threat detection in data streams, an IBM Outstanding Innovation Award in 2008 for his scientific contributions to privacy technology, and two IBM Outstanding Technical Achievement Awards in 2009 and 2015 for his work on data streams/high-dimensional data.
For his work on condensation-based privacy-preserving data mining, he got the EDBT 2014 Test of Time Award. He has also received the IEEE ICDM Research Contributions Award (2015) and the ACM SIGKDD Innovations Award (2019), the two highest prizes for outstanding research achievements in 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 (2015). From 2004 to 2008, he was an associate editor for IEEE Transactions on Knowledge and Data Engineering. He is an associate editor for the IEEE Transactions on Big Data, an action editor for the Data Mining and Knowledge Discovery Journal, and an associate editor for the Journal of Knowledge and Information Systems.
He is the editor-in-chief of both the ACM Transactions on Knowledge Discovery from Data and the ACM SIGKDD Explorations journals. He is a member of the advisory board for Springer's Lecture Notes on Social Networks. He is a member of the SIAM industry committee and has served as vice-president of the SIAM Activity Group on Data Mining. For contributions to knowledge discovery and data mining methods, he is a SIAM, ACM, and IEEE fellow.
| SKU | Unavailable |
| ISBN 13 | 9783030966256 |
| ISBN 10 | 3030966259 |
| Title | Machine Learning for Text |
| Author | Charu C Aggarwal |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2023-05-06 |
| Number of pages | 565 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |