Production NLP with spaCy

A Comprehensive Guide to Building Production-Ready NLP Systems

Introduction: Why spaCy, Why Now

1. What Is Natural Language Processing?
2. Why spaCy for Production NLP?
3. When spaCy Is Not the Right Tool
4. Comparing spaCy to Alternatives
5. What You Will Learn in This Book
6. How to Use This Book
7. A Note on spaCy Versions
8. Chapter Summary

Chapter 1: Getting Started with NLP and spaCy

1. What Is Natural Language Processing?
2. Why spaCy for Production NLP?
3. Comparing spaCy to NLTK, Stanza, Hugging Face Transformers
4. Installation and Environment Setup
5. Your First spaCy Program: End-to-End Example
6. Understanding the spaCy Processing Flow
7. Chapter Summary

Chapter 2: Core Concepts: Language Models, Tokenization, and Document Objects

1. The spaCy Pipeline Architecture
2. Loading and Understanding Language Models
3. The Doc Object: Your Processed Document
4. Tokens: The Atomic Units of Analysis
5. Spans: Working with Text Ranges
6. Tokenization Mechanics and Edge Cases
7. Chapter Summary

Chapter 3: Lexical Analysis and Text Attributes

1. Token Attributes: orth, lemma, shape, length, and Beyond
2. Special Character Detection (Punctuation, Whitespace, Quotes)
3. Case Normalization and Unicode Handling
4. Building Text Preprocessing Pipelines
5. Practical Applications: Data Cleaning and Normalization
6. Multilingual NLP with spaCy: Tokenization, Models, and Challenges
7. Chapter Summary

Chapter 4: Part-of-Speech Tagging and Morphological Analysis

1. Understanding Parts of Speech
2. Universal POS Tags vs Fine-Grained Tags
3. Lemmatization: Getting to Word Roots
4. Morphological Features (Tense, Number, Gender, Case)
5. Using POS Information in Real Applications
6. Chapter Summary

Chapter 5: Dependency Parsing and Syntactic Structure

1. What Is Dependency Grammar?
2. Reading Dependency Trees
3. Core Relations: Subject, Object, Modifiers
4. Navigating the Tree Programmatically
5. Advanced Traversal Patterns and Use Cases
6. Chapter Summary

Chapter 6: Named Entity Recognition

1. What Is Named Entity Recognition?
2. Built-in Entity Types and Their Meaning
3. Extracting and Working with Entities
4. Evaluating NER Performance
5. Handling Ambiguity and Edge Cases
6. Customizing Entity Types
7. Chapter Summary

Chapter 7: Rule-Based Matching and Pattern Extraction

1. When to Use Rules vs Machine Learning
2. The Matcher: Token-Level Pattern Matching
3. The PhraseMatcher: Fast Phrase Detection
4. The DependencyMatcher: Syntax-Aware Patterns
5. Combining Matchers in Production Pipelines
6. Chapter Summary

Chapter 8: Text Classification, Similarity, and Embeddings

1. Trainable Text Classification with TextCategorizer
2. Binary vs Multi-Label Classification
3. Word Vectors and Embeddings Explained
4. Computing Similarity Between Texts
5. Practical Applications: Sentiment, Topic Detection
6. Chapter Summary

Chapter 9: Transformers and Modern Architecture Integration

1. Why Transformers Changed NLP
2. Using Transformer Models in spaCy Pipelines
3. spaCy’s Hugging Face Integration
4. Performance vs Accuracy Trade-offs
5. Fine-Tuning Transformer-Based Pipelines
6. Chapter Summary

Chapter 10: Training and Fine-Tuning Custom Models

1. Preparing Training Data for spaCy
2. Creating Annotation Guidelines
3. Measuring Inter-Annotator Agreement
4. Using Prodigy and Other Annotation Tools
5. Training Custom NER Models
6. Fine-Tuning Existing Pipelines
7. Evaluation Metrics and Model Selection
8. Error Analysis: Understanding Model Failures
9. Active Learning Strategies
10. Detecting Bias in Training Data and Models
11. Active Learning with spaCy Training
12. Common Training Pitfalls and Solutions
13. Chapter Summary

Chapter 11: Entity Linking and Relation Extraction

1. What Is Entity Linking?
2. spaCy’s Entity Ruler and Entity Linker
3. Connecting Entities to Wikidata
4. Relation Extraction Concepts
5. Building Knowledge Graphs from Text
6. Chapter Summary

Chapter 12: Custom Pipeline Components, Extensions, and Configuration

1. Creating Custom Pipeline Components
2. Adding Extensions to Doc, Span, and Token
3. The spaCy Configuration System (Config)
4. Building Reproducible Pipelines with Config Files
5. Advanced Pipeline Composition Patterns
6. Chapter Summary

Chapter 13: Project Workflows and Reproducibility

1. Understanding spaCy Projects
2. Setting Up a Project with spacy project init
3. Custom Recipes and Automation
4. Versioning and Sharing Models
5. CI/CD for NLP Pipelines
6. Chapter Summary

Chapter 14: Production Deployment and Performance Optimization

1. Deployment Options: FastAPI, Flask, spaCy Serving
2. Batch Processing Strategies
3. Memory Optimization and Model Disassembly
4. Performance Profiling and Tuning
5. Monitoring and Maintaining Production Models
6. Advanced Data Drift Detection
7. Chapter Summary

Chapter 15: Real-World Applications and Integration Patterns

1. Information Extraction and Document Processing
2. Search Enhancement and Semantic Retrieval
3. Chatbots and Conversational NLP
4. Domain Applications: Healthcare, Finance, Legal, HR
5. Integration with pandas, scikit-learn, Streamlit
6. Chapter Summary

Conclusion: Building NLP Systems That Last

1. Architectural Principles for Production NLP
2. The Evolving NLP Landscape
3. A Framework for Decision-Making
4. Continuing Your Journey
5. Final Thoughts

References