000			02037nam a22002297a 4500
999			_c1564 _d1564
005			20220204103738.0
008			220204b ||||| |||| 00| 0 eng d
020			_a9783030088071
082			_a006.31 _bAGG
100			_aAggarwal, Charu C. _94493
245			_aMachine learning for text
260			_bSpringer _a2018 _cSwitzerland
300			_axxiii, 493 p.
365			_aEURO _b49.99
520			_aIntroduction 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.
650			_aMachine learning _92343
650			_aText processing (Computer science) _95193
650			_aArtificial intelligence _91478
650			_aComputer science _91018
650			_aData mining _9365
942			_2ddc _cBK