Practical Natural Language Processing. A Comprehensive Guide to Building Real-World NLP Systems

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Spis treści książki

• Foreword
• Preface
  • Why We Wrote This Book
  • The Philosophy
  • Scope
  • Who Should Read This Book
  • What You Will Learn
  • Structure of the Book
  • How to Read This Book
    • Conventions Used in This Book
    • Using Code Examples
    • OReilly Online Learning
    • How to Contact Us
    • Further Information
    • Acknowledgments
• I. Foundations
• 1. NLP: A Primer
  • NLP in the Real World
    • NLP Tasks
  • What Is Language?
    • Building Blocks of Language
      • Phonemes
      • Morphemes and lexemes
      • Syntax
      • Context
    • Why Is NLP Challenging?
      • Ambiguity
      • Common knowledge
      • Creativity
      • Diversity across languages
  • Machine Learning, Deep Learning, and NLP: An Overview
  • Approaches to NLP
    • Heuristics-Based NLP
    • Machine Learning for NLP
      • Naive Bayes
      • Support vector machine
      • Hidden Markov Model
      • Conditional random fields
    • Deep Learning for NLP
      • Recurrent neural networks
      • Long short-term memory
      • Convolutional neural networks
      • Transformers
      • Autoencoders
    • Why Deep Learning Is Not Yet the Silver Bullet for NLP
  • An NLP Walkthrough: Conversational Agents
  • Wrapping Up
• 2. NLP Pipeline
  • Data Acquisition
  • Text Extraction and Cleanup
    • HTML Parsing and Cleanup
    • Unicode Normalization
    • Spelling Correction
    • System-Specific Error Correction
  • Pre-Processing
    • Preliminaries
      • Sentence segmentation
      • Word tokenization
    • Frequent Steps
      • Stemming and lemmatization
    • Other Pre-Processing Steps
      • Text normalization
      • Language detection
      • Code mixing and transliteration
    • Advanced Processing
  • Feature Engineering
    • Classical NLP/ML Pipeline
    • DL Pipeline
  • Modeling
    • Start with Simple Heuristics
    • Building Your Model
    • Building THE Model
  • Evaluation
    • Intrinsic Evaluation
    • Extrinsic Evaluation
  • Post-Modeling Phases
    • Deployment
    • Monitoring
    • Model Updating
  • Working with Other Languages
  • Case Study
  • Wrapping Up
• 3. Text Representation
  • Vector Space Models
  • Basic Vectorization Approaches
    • One-Hot Encoding
    • Bag of Words
    • Bag of N-Grams
    • TF-IDF
  • Distributed Representations
    • Word Embeddings
      • Pre-trained word embeddings
      • Training our own embeddings
        • CBOW
        • SkipGram
    • Going Beyond Words
  • Distributed Representations Beyond Words and Characters
  • Universal Text Representations
  • Visualizing Embeddings
  • Handcrafted Feature Representations
  • Wrapping Up
• II. Essentials
• 4. Text Classification
  • Applications
  • A Pipeline for Building Text Classification Systems
    • A Simple Classifier Without the Text Classification Pipeline
    • Using Existing Text Classification APIs
  • One Pipeline, Many Classifiers
    • Naive Bayes Classifier
    • Logistic Regression
    • Support Vector Machine
  • Using Neural Embeddings in Text Classification
    • Word Embeddings
    • Subword Embeddings and fastText
    • Document Embeddings
  • Deep Learning for Text Classification
    • CNNs for Text Classification
    • LSTMs for Text Classification
    • Text Classification with Large, Pre-Trained Language Models
  • Interpreting Text Classification Models
    • Explaining Classifier Predictions with Lime
  • Learning with No or Less Data and Adapting to New Domains
    • No Training Data
    • Less Training Data: Active Learning and Domain Adaptation
  • Case Study: Corporate Ticketing
  • Practical Advice
  • Wrapping Up
• 5. Information Extraction
  • IE Applications
  • IE Tasks
  • The General Pipeline for IE
  • Keyphrase Extraction
    • Implementing KPE
    • Practical Advice
  • Named Entity Recognition
    • Building an NER System
    • NER Using an Existing Library
    • NER Using Active Learning
    • Practical Advice
  • Named Entity Disambiguation and Linking
    • NEL Using Azure API
  • Relationship Extraction
    • Approaches to RE
    • RE with the Watson API
  • Other Advanced IE Tasks
    • Temporal Information Extraction
    • Event Extraction
    • Template Filling
  • Case Study
  • Wrapping Up
• 6. Chatbots
  • Applications
    • A Simple FAQ Bot
  • A Taxonomy of Chatbots
    • Goal-Oriented Dialog
    • Chitchats
  • A Pipeline for Building Dialog Systems
  • Dialog Systems in Detail
    • PizzaStop Chatbot
      • Building our Dialogflow agent
      • Testing our agent
  • Deep Dive into Components of a Dialog System
    • Dialog Act Classification
    • Identifying Slots
    • Response Generation
    • Dialog Examples with Code Walkthrough
      • Datasets
      • Dialog act prediction
        • Loading the dataset
        • Models
      • Slot identification
        • Loading the dataset
        • Models
  • Other Dialog Pipelines
    • End-to-End Approach
    • Deep Reinforcement Learning for Dialogue Generation
    • Human-in-the-Loop
  • Rasa NLU
  • A Case Study: Recipe Recommendations
    • Utilizing Existing Frameworks
    • Open-Ended Generative Chatbots
  • Wrapping Up
• 7. Topics in Brief
  • Search and Information Retrieval
    • Components of a Search Engine
    • A Typical Enterprise Search Pipeline
    • Setting Up a Search Engine: An Example
    • A Case Study: Book Store Search
  • Topic Modeling
    • Training a Topic Model: An Example
    • Whats Next?
  • Text Summarization
    • Summarization Use Cases
    • Setting Up a Summarizer: An Example
    • Practical Advice
  • Recommender Systems for Textual Data
    • Creating a Book Recommender System: An Example
    • Practical Advice
  • Machine Translation
    • Using a Machine Translation API: An Example
    • Practical Advice
  • Question-Answering Systems
    • Developing a Custom Question-Answering System
    • Looking for Deeper Answers
  • Wrapping Up
• III. Applied
• 8. Social Media
  • Applications
  • Unique Challenges
  • NLP for Social Data
    • Word Cloud
    • Tokenizer for SMTD
    • Trending Topics
    • Understanding Twitter Sentiment
    • Pre-Processing SMTD
      • Removing markup elements
      • Handling non-text data
      • Handling apostrophes
      • Handling emojis
      • Split-joined words
      • Removal of URLs
      • Nonstandard spellings
    • Text Representation for SMTD
    • Customer Support on Social Channels
  • Memes and Fake News
    • Identifying Memes
    • Fake News
  • Wrapping Up
• 9. E-Commerce and Retail
  • E-Commerce Catalog
    • Review Analysis
    • Product Search
    • Product Recommendations
  • Search in E-Commerce
  • Building an E-Commerce Catalog
    • Attribute Extraction
      • Direct attribute extraction
      • Indirect attribute extraction
    • Product Categorization and Taxonomy
    • Product Enrichment
    • Product Deduplication and Matching
      • Attribute match
      • Title match
      • Image match
  • Review Analysis
    • Sentiment Analysis
    • Aspect-Level Sentiment Analysis
      • Supervised approach
      • Unsupervised approach
    • Connecting Overall Ratings to Aspects
    • Understanding Aspects
  • Recommendations for E-Commerce
    • A Case Study: Substitutes and Complements
      • Latent attribute extraction from reviews
      • Product linking
  • Wrapping Up
• 10. Healthcare, Finance, and Law
  • Healthcare
    • Health and Medical Records
    • Patient Prioritization and Billing
    • Pharmacovigilance
    • Clinical Decision Support Systems
    • Health Assistants
    • Electronic Health Records
      • HARVEST: Longitudinal report understanding
      • Question answering for health
      • Outcome prediction and best practices
    • Mental Healthcare Monitoring
    • Medical Information Extraction and Analysis
  • Finance and Law
    • NLP Applications in Finance
      • Financial sentiment
      • Risk assessments
      • Accounting and auditing
    • NLP and the Legal Landscape
      • Legal entity extraction with LexNLP
  • Wrapping Up
• IV. Bringing It All Together
• 11. The End-to-End NLP Process
  • Revisiting the NLP Pipeline: Deploying NLP Software
    • An Example Scenario
  • Building and Maintaining a Mature System
    • Finding Better Features
    • Iterating Existing Models
    • Code and Model Reproducibility
    • Troubleshooting and Interpretability
    • Monitoring
    • Minimizing Technical Debt
    • Automating Machine Learning
      • auto-sklearn
      • Google Cloud AutoML and other techniques
  • The Data Science Process
    • The KDD Process
    • Microsoft Team Data Science Process
  • Making AI Succeed at Your Organization
    • Team
    • Right Problem and Right Expectations
    • Data and Timing
    • A Good Process
    • Other Aspects
  • Peeking over the Horizon
  • Final Words
• Index

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