000			02081 a2200265 4500
999			_c53209 _d53209
008			200731b ||||| |||| 00| 0 eng d
020			_a9783030088071
082			_a 006.312 _bAGG
100			_aAggarwal, Charu C.
245			_aMachine learning for text
260			_bSpringer, _c2018. _aCham, Switzerland:
300			_axxiii, 493 p. _bpb; _c25 cm.
365			_aEURO _b69.99
520			_aText 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			_aArtificial Intelligence
650			_aData Mining
650			_aMachine Learning
650			_aComputer Science _93
650			_aDatabase Management
650			_a Engineering & Applied Sciences
650			_aIntelligence & Semantics
650			_aData Mining & Knowledge Discovery
650			_aInformation Filtering
942			_2ddc _cTD