Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series)
Eisenstein, Jacob
ISBN 10: 0262042843 ISBN 13: 9780262042840
Published by The MIT Press, 2019
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A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques.
This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation.
The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.
About the Author: Jacob Eisenstein works at Google as a research scientist. He was previously on the faculty in the School of Interactive Computing at Georgia Institute of Technology.
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Title: Introduction to Natural Language Processing ...
Publisher: The MIT Press
Publication Date: 2019
Binding: hardcover
Condition: New
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Introduction to Natural Language Processing
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Hardcover. Condition: Fair. Unterschrift / Widmung ohne Bezug; Spuren von Feuchtigkeit / Nässe; Aus Bibliothek aussortiert; Leichte Kratzer / Abnutzungen / Druckstellen. This textbook surveys computational methods for understanding, generating, and manipulating human language, combining classical representations and algorithms with modern machine learning techniques. It provides a technical perspective on natural language processing, emphasizing data-driven approaches through supervised and unsupervised machine learning. The first section lays a foundation in machine learning, equipping readers with tools for word-based textual analysis. The second section covers structured representations of language, including sequences, trees, and graphs. The third section delves into linguistic meaning representation and analysis, exploring formal logic and neural word embeddings. The final section presents in-depth treatments of three key applications: information extraction, machine translation, and text generation. End-of-chapter exercises encourage both theoretical analysis and software implementation. The text synthesizes a wide range of research, connecting contemporary machine learning techniques with linguistic and computational foundations. It is suitable for advanced undergraduate and graduate courses, as well as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. Mastery of the material will enable students to build and analyze novel natural language processing systems and comprehend the latest research i. Seller Inventory # ee760044-5cce-4671-ab09-fc12528adfd5
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ISBN 10: 0262042843
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