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Adaptive Computation and Machine Learning
Introduction to Natural Language Processing
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.
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.
536 pages, Hardcover
Published October 1, 2019
13 people are currently reading
195 people want to read
About the author
Jacob Eisenstein
2 books1 followerJacob 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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Displaying 1 - 2 of 2 reviews
February 6, 2020
Extremely mixed. First chapter is great, a nice high-level summary of the difficult history of getting computers to understand us, and the many fields and field factions involved. (Linguistics is a deep science that in large part taught CS how to do theory, but certain of its dogmas - against probabilities, against machine learning - ended up holding it back for decades.)
But chapters 2-5 are bad: weird notation, and almost no diagrams for lots of natively geometric ideas. That said, fig 3.3. is a great essence of backprop. I switched to Jurafsky afterward.
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But chapters 2-5 are bad: weird notation, and almost no diagrams for lots of natively geometric ideas. That said, fig 3.3. is a great essence of backprop. I switched to Jurafsky afterward.
[Free here]
June 27, 2023
Very good and useful
Displaying 1 - 2 of 2 reviews
