02217nam a22002895i 4500 28239 28210 20306424 EG-ScBUE 20200305144639.0 180125t20182018sz a f b 001 0 eng d 9783319735306 DLC eng rda DLC EG-ScBUE 006.31 AGG 22 Aggarwal, Charu C., author. Machine learning for text / Charu C. Aggarwal. Cham, Switzerland : Springer / Springer International Publishing, [2018] c2018 xxiii, 493 pages : illustrations ; 28 cm text txt rdacontent unmediated n rdamedia volume nc rdacarrier 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. Machine learning. BUEsh Text processing (Computer science) BUEsh 7933 COMSCI March2020 ddc BB 0 0 ddc 0 0 Baccah MAIN MAIN LOW 2020-03-05 Purchase 0 006.31 AGG 000048898 2025-07-15 00:00:00 2020-03-05 BB