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Introduction To Natural Language Processing (Adaptive Computation And Machine Learning Series),New
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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 Processingmethods For Building Computer Software That Understands, Generates, And Manipulates Human Language. It Emphasizes Contemporary Datadriven 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 Wordbased 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 Chapterlength Treatments Of Three Transformative Applications Of Natural Language Processing: Information Extraction, Machine Translation, And Text Generation. Endofchapter Exercises Include Both Paperandpencil 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 Graduatelevel Courses And As A Reference For Software Engineers And Data Scientists. Readers Should Have A Background In Computer Programming And Collegelevel 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.
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does the book have? A: This book has five hundred thirty-six pages. It provides a comprehensive look at natural language processing methods and techniques.
- Q: What is the binding type of this book? A: The binding type is hardcover. This ensures durability and longevity for reference purposes.
- Q: What are the dimensions of the book? A: The book measures seven point twenty-five inches in length, one point sixteen inches in width, and nine point thirty-one inches in height. These dimensions make it easy to handle and store.
- Q: What is the reading level for this book? A: This book is suitable for advanced undergraduate and graduate-level courses. It is designed for readers with a background in computer programming and college-level mathematics.
- Q: Can beginners understand the content of this book? A: No, this book is not recommended for beginners. It requires prior knowledge of programming and mathematics to grasp the complex concepts.
- Q: What topics are covered in the book? A: The book covers computational methods, data-driven approaches, and applications like information extraction and machine translation. It provides a thorough synthesis of the field.
- Q: How should I care for this book? A: To care for this book, keep it in a dry place and avoid exposure to direct sunlight. This will help preserve its quality over time.
- Q: Is there a warranty for this book? A: No, there is no warranty for this book. However, it is a quality publication from MIT Press.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, contact the seller for possible returns or exchanges. Ensure to have your order details ready when reaching out.
- Q: Is this book suitable for self-study? A: Yes, this book can be used for self-study. It includes end-of-chapter exercises for practical application of the concepts.
- Q: How are the exercises structured in the book? A: The exercises include both theoretical analysis and software implementation tasks. This helps readers apply what they learn effectively.
- Q: Does the book include references to recent research? A: Yes, the book synthesizes and links contemporary machine learning techniques with foundational linguistic research. It serves as a valuable resource for understanding current trends.
- Q: What is the author’s background? A: The author, Jacob Eisenstein, is an expert in natural language processing and machine learning. His insights contribute significantly to the book's credibility.
- Q: Is this book useful for data scientists? A: Yes, this book is a great reference for software engineers and data scientists. It offers practical tools and techniques relevant to the field.