Aggarwal, Charu C. <br/><br/>
Machine learning for text 

 - 2nd ed. 
 - Switzerland:  Springer Nature,  2023. 
 - xxiii, 565 p. 
<br/><br/> Includes bibliographical references and index. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783030966225 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence<br/>Data mining<br/>Data Mining and Knowledge Discovery
<br/><br/><label>Dewey Class. No.: </label>006.312 AGG  / AGG