Isbn: 9783030966225 - machine learning for text (10 Ergebnisse)

Hardcover. Zustand: Very Good. 2nd ed. 2022. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

hardcover. Zustand: Fine.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Zustand: Brand New. 2nd edition. 565 pages. 10.00x7.00x1.50 inches. In Stock.

Zustand: NEW.

Gebunden. Zustand: New.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This second edition textbook covers a coherently organized framework for text analytics, which integratesmaterial drawn from the intersecting topics of information retrieval, machine learning, andnatural language processing. Particular importance is placed on deep learning methods. Thechapters 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.2. Domain-sensitive learning and information retrieval: 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. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection.Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially morematerial on deep learning and natural language processing. Significant focus isplaced on topics like transformers, pre-trained language models, knowledge graphs,and question answering. …