Foreword Preface Part I. Foundations 1. NLP: A Primer NLP in the Real World NLP Tasks What Is Language? Building Blocks of Language Why Is NLP Challenging? Machine Learning, Deep Learning, and NLP: An Overview Approaches to NLP Heuristics-Based NLP Machine Learning for NLP Deep Learning for NLP 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 Frequent Steps Other Pre-Processing Steps 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 Going Beyond Words Distributed Representations Beyond Words and Characters Universal Text Representations Visualizing Embeddings Handcrafted Feature Representations Wrapping Up Part II.Essentials Part III.Applied Part IV.Bringing It All Together Index
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