Natural Language Processing with Spark NLP by Thomas, Alex -- Read -- Imperial Library of Trantor

Index

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

I. Basics 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

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

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

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

II. Building Blocks 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

6. Information Retrieval

Inverted Indices

Building an Inverted Index

Vector Space Model

Stop-Word Removal Inverse Document Frequency In Spark

Exercises Resources

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

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

9. Information Extraction

Named-Entity Recognition Coreference Resolution Assertion Status Detection Relationship Extraction Summary Exercises

10. Topic Modeling

K-Means Latent Semantic Indexing Nonnegative Matrix Factorization Latent Dirichlet Allocation Exercises

11. Word Embeddings

Word2vec GloVe fastText Transformers ELMo, BERT, and XLNet doc2vec Exercises

III. Applications 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

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

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

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

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

IV. Building NLP Systems 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

18. Human Labeling

Guidelines Scenario: Academic Paper Classification Inter-Labeler Agreement Iterative Labeling Labeling Text

Classification Tagging

Checklist Conclusion

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

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