Applied Text Analysis with Python. Enabling Language-Aware Data Products with Machine Learning

Spis treści ebooka

• Preface
  • Computational Challenges of Natural Language
    • Linguistic Data: Tokens and Words
    • Enter Machine Learning
  • Tools for Text Analysis
  • What to Expect from This Book
  • Who This Book Is For
  • Code Examples and GitHub Repository
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• 1. Language and Computation
  • The Data Science Paradigm
  • Language-Aware Data Products
    • The Data Product Pipeline
      • The model selection triple
  • Language as Data
    • A Computational Model of Language
    • Language Features
    • Contextual Features
    • Structural Features
  • Conclusion
• 2. Building a Custom Corpus
  • What Is a Corpus?
    • Domain-Specific Corpora
    • The Baleen Ingestion Engine
  • Corpus Data Management
    • Corpus Disk Structure
      • The Baleen disk structure
  • Corpus Readers
    • Streaming Data Access with NLTK
    • Reading an HTML Corpus
      • Corpus monitoring
    • Reading a Corpus from a Database
  • Conclusion
• 3. Corpus Preprocessing and Wrangling
  • Breaking Down Documents
    • Identifying and Extracting Core Content
    • Deconstructing Documents into Paragraphs
    • Segmentation: Breaking Out Sentences
    • Tokenization: Identifying Individual Tokens
    • Part-of-Speech Tagging
    • Intermediate Corpus Analytics
  • Corpus Transformation
    • Intermediate Preprocessing and Storage
      • Writing to pickle
    • Reading the Processed Corpus
  • Conclusion
• 4. Text Vectorization and Transformation Pipelines
  • Words in Space
    • Frequency Vectors
      • With NLTK
      • In Scikit-Learn
      • The Gensim way
    • One-Hot Encoding
      • With NLTK
      • In Scikit-Learn
      • The Gensim way
    • Term FrequencyInverse Document Frequency
      • With NLTK
      • In Scikit-Learn
      • The Gensim way
    • Distributed Representation
      • The Gensim way
  • The Scikit-Learn API
    • The BaseEstimator Interface
    • Extending TransformerMixin
      • Creating a custom Gensim vectorization transformer
      • Creating a custom text normalization transformer
  • Pipelines
    • Pipeline Basics
    • Grid Search for Hyperparameter Optimization
    • Enriching Feature Extraction with Feature Unions
  • Conclusion
• 5. Classification for Text Analysis
  • Text Classification
    • Identifying Classification Problems
    • Classifier Models
  • Building a Text Classification Application
    • Cross-Validation
      • Streaming access to k splits
    • Model Construction
    • Model Evaluation
    • Model Operationalization
  • Conclusion
• 6. Clustering for Text Similarity
  • Unsupervised Learning on Text
  • Clustering by Document Similarity
    • Distance Metrics
    • Partitive Clustering
      • k-means clustering
      • Optimizing k-means
      • Handling uneven geometries
    • Hierarchical Clustering
      • Agglomerative clustering
  • Modeling Document Topics
    • Latent Dirichlet Allocation
      • In Scikit-Learn
      • The Gensim way
      • Visualizing topics
    • Latent Semantic Analysis
      • In Scikit-Learn
      • The Gensim way
    • Non-Negative Matrix Factorization
      • In Scikit-Learn
  • Conclusion
• 7. Context-Aware Text Analysis
  • Grammar-Based Feature Extraction
    • Context-Free Grammars
    • Syntactic Parsers
    • Extracting Keyphrases
    • Extracting Entities
  • n-Gram Feature Extraction
    • An n-Gram-Aware CorpusReader
    • Choosing the Right n-Gram Window
    • Significant Collocations
  • n-Gram Language Models
    • Frequency and Conditional Frequency
    • Estimating Maximum Likelihood
    • Unknown Words: Back-off and Smoothing
    • Language Generation
  • Conclusion
• 8. Text Visualization
  • Visualizing Feature Space
    • Visual Feature Analysis
      • n-gram viewer
      • Network visualization
      • Co-occurrence plots
      • Text x-rays and dispersion plots
    • Guided Feature Engineering
      • Part-of-speech tagging
      • Most informative features
  • Model Diagnostics
    • Visualizing Clusters
    • Visualizing Classes
    • Diagnosing Classification Error
      • Classification report heatmaps
      • Confusion matrices
  • Visual Steering
    • Silhouette Scores and Elbow Curves
      • Silhouette scores
      • Elbow curves
  • Conclusion
• 9. Graph Analysis of Text
  • Graph Computation and Analysis
    • Creating a Graph-Based Thesaurus
    • Analyzing Graph Structure
    • Visual Analysis of Graphs
  • Extracting Graphs from Text
    • Creating a Social Graph
      • Finding entity pairs
      • Property graphs
      • Implementing the graph extraction
    • Insights from the Social Graph
      • Centrality
      • Structural analysis
  • Entity Resolution
    • Entity Resolution on a Graph
    • Blocking with Structure
    • Fuzzy Blocking
  • Conclusion
• 10. Chatbots
  • Fundamentals of Conversation
    • Dialog: A Brief Exchange
    • Maintaining a Conversation
  • Rules for Polite Conversation
    • Greetings and Salutations
    • Handling Miscommunication
  • Entertaining Questions
    • Dependency Parsing
    • Constituency Parsing
    • Question Detection
    • From Tablespoons to Grams
  • Learning to Help
    • Being Neighborly
    • Offering Recommendations
  • Conclusion
• 11. Scaling Text Analytics with Multiprocessing and Spark
  • Python Multiprocessing
    • Running Tasks in Parallel
    • Process Pools and Queues
    • Parallel Corpus Preprocessing
  • Cluster Computing with Spark
    • Anatomy of a Spark Job
    • Distributing the Corpus
    • RDD Operations
    • NLP with Spark
      • From Scikit-Learn to MLLib
      • Feature extraction
      • Text clustering with MLLib
      • Text classification with MLLib
      • Local fit, global evaluation
  • Conclusion
• 12. Deep Learning and Beyond
  • Applied Neural Networks
  • Neural Language Models
    • Artificial Neural Networks
      • Training a multilayer perceptron
    • Deep Learning Architectures
      • TensorFlow: A framework for deep learning
      • Keras: An API for deep learning
  • Sentiment Analysis
    • Deep Structure Analysis
      • Predicting sentiment with a bag-of-keyphrases
  • The Future Is (Almost) Here
• Glossary
• Index

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