000			01191nam a2200217 4500
005			20250711191320.0
008			250711b |||||||| |||| 00| 0 eng d
020			_a9783030966225
082			_a006.312 AGG _bAGG
100			_aAggarwal, Charu C.
245			_aMachine learning for text
250			_a2nd ed.
260			_aSwitzerland: _bSpringer Nature, _c2023.
300			_axxiii, 565 p.
500			_aIncludes bibliographical references and index.
520			_aThis 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.
650			_aArtificial intelligence
650			_aData mining
650			_aData Mining and Knowledge Discovery
942			_2ddc _n0 _cC
999			_c30521 _d30521