NATURAL LANGUAGE PROCESSING W/

Cover
Copyright
Table of Contents
Preface
	Why Natural Language Processing Is Important and Difficult
	Background
	Philosophy
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Part I. Basics
	Chapter 1. Getting Started
		Introduction
		Other Tools
		Setting Up Your Environment
			Prerequisites
			Starting Apache Spark
			Checking Out the Code
		Getting Familiar with Apache Spark
			Starting Apache Spark with Spark NLP
			Loading and Viewing Data in Apache Spark
		Hello World with Spark NLP
	Chapter 2. Natural Language Basics
		What Is Natural Language?
			Origins of Language
			Spoken Language Versus Written Language
		Linguistics
			Phonetics and Phonology
			Morphology
			Syntax
			Semantics
		Sociolinguistics: Dialects, Registers, and Other Varieties
			Formality
			Context
		Pragmatics
			Roman Jakobson
			How To Use Pragmatics
		Writing Systems
			Origins
			Alphabets
			Abjads
			Abugidas
			Syllabaries
			Logographs
		Encodings
			ASCII
			Unicode
			UTF-8
		Exercises: Tokenizing
			Tokenize English
			Tokenize Greek
			Tokenize Ge’ez (Amharic)
		Resources
	Chapter 3. NLP on Apache Spark
		Parallelism, Concurrency, Distributing Computation
			Parallelization Before Apache Hadoop
			MapReduce and Apache Hadoop
			Apache Spark
		Architecture of Apache Spark
			Physical Architecture
			Logical Architecture
		Spark SQL and Spark MLlib
			Transformers
			Estimators and Models
			Evaluators
		NLP Libraries
			Functionality Libraries
			Annotation Libraries
			NLP in Other Libraries
		Spark NLP
			Annotation Library
			Stages
			Pretrained Pipelines
			Finisher
		Exercises: Build a Topic Model
		Resources
	Chapter 4. Deep Learning Basics
		Gradient Descent
		Backpropagation
		Convolutional Neural Networks
			Filters
			Pooling
		Recurrent Neural Networks
			Backpropagation Through Time
			Elman Nets
			LSTMs
		Exercise 1
		Exercise 2
		Resources
Part II. Building Blocks
	Chapter 5. Processing Words
		Tokenization
		Vocabulary Reduction
			Stemming
			Lemmatization
			Stemming Versus Lemmatization
			Spelling Correction
			Normalization
		Bag-of-Words
		CountVectorizer
		N-Gram
		Visualizing: Word and Document Distributions
		Exercises
		Resources
	Chapter 6. Information Retrieval
		Inverted Indices
			Building an Inverted Index
		Vector Space Model
			Stop-Word Removal
			Inverse Document Frequency
			In Spark
		Exercises
		Resources
	Chapter 7. Classification and Regression
		Bag-of-Words Features
		Regular Expression Features
		Feature Selection
		Modeling
			Naïve Bayes
			Linear Models
			Decision/Regression Trees
			Deep Learning Algorithms
		Iteration
		Exercises
	Chapter 8. Sequence Modeling with Keras
		Sentence Segmentation
			(Hidden) Markov Models
		Section Segmentation
		Part-of-Speech Tagging
		Conditional Random Field
		Chunking and Syntactic Parsing
		Language Models
		Recurrent Neural Networks
		Exercise: Character N-Grams
		Exercise: Word Language Model
		Resources
	Chapter 9. Information Extraction
		Named-Entity Recognition
		Coreference Resolution
		Assertion Status Detection
		Relationship Extraction
		Summary
		Exercises
	Chapter 10. Topic Modeling
		K-Means
		Latent Semantic Indexing
		Nonnegative Matrix Factorization
		Latent Dirichlet Allocation
		Exercises
	Chapter 11. Word Embeddings
		Word2vec
		GloVe
		fastText
		Transformers
		ELMo, BERT, and XLNet
		doc2vec
		Exercises
Part III. Applications
	Chapter 12. Sentiment Analysis and Emotion Detection
		Problem Statement and Constraints
		Plan the Project
		Design the Solution
		Implement the Solution
		Test and Measure the Solution
			Business Metrics
			Model-Centric Metrics
			Infrastructure Metrics
			Process Metrics
			Offline Versus Online Model Measurement
		Review
			Initial Deployment
			Fallback Plans
			Next Steps
		Conclusion
	Chapter 13. Building Knowledge Bases
		Problem Statement and Constraints
		Plan the Project
		Design the Solution
		Implement the Solution
		Test and Measure the Solution
			Business Metrics
			Model-Centric Metrics
			Infrastructure Metrics
			Process Metrics
		Review
		Conclusion
	Chapter 14. Search Engine
		Problem Statement and Constraints
		Plan the Project
		Design the Solution
		Implement the Solution
		Test and Measure the Solution
			Business Metrics
			Model-Centric Metrics
		Review
		Conclusion
	Chapter 15. Chatbot
		Problem Statement and Constraints
		Plan the Project
		Design the Solution
		Implement the Solution
		Test and Measure the Solution
			Business Metrics
			Model-Centric Metrics
		Review
		Conclusion
	Chapter 16. Object Character Recognition
		Kinds of OCR Tasks
			Images of Printed Text and PDFs to Text
			Images of Handwritten Text to Text
			Images of Text in Environment to Text
			Images of Text to Target
			Note on Different Writing Systems
		Problem Statement and Constraints
		Plan the Project
		Implement the Solution
		Test and Measure the Solution
		Model-Centric Metrics
		Review
		Conclusion
Part IV. Building NLP Systems
	Chapter 17. Supporting Multiple Languages
		Language Typology
		Scenario: Academic Paper Classification
		Text Processing in Different Languages
			Compound Words
			Morphological Complexity
		Transfer Learning and Multilingual Deep Learning
		Search Across Languages
		Checklist
		Conclusion
	Chapter 18. Human Labeling
		Guidelines
		Scenario: Academic Paper Classification
		Inter-Labeler Agreement
		Iterative Labeling
		Labeling Text
			Classification
			Tagging
		Checklist
		Conclusion
	Chapter 19. Productionizing NLP Applications
		Spark NLP Model Cache
		Spark NLP and TensorFlow Integration
			Spark Optimization Basics
			Design-Level Optimization
			Profiling Tools
			Monitoring
			Managing Data Resources
			Testing NLP-Based Applications
			Unit Tests
			Integration Tests
			Smoke and Sanity Tests
			Performance Tests
			Usability Tests
			Demoing NLP-Based Applications
		Checklists
			Model Deployment Checklist
			Scaling and Performance Checklist
			Testing Checklist
		Conclusion
Glossary
Index
About the Author
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