Practical Natural Language Processing Chorzów

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You...ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail.With this book, you...ll:Understand the wide spectrum of problem statements, tasks, and solution approaches within NLPImplement and evaluate different NLP applications using machine learning and deep learning methodsFine-tune your NLP solution based on your business problem and industry verticalEvaluate various algorithms and approaches for NLP product tasks, datasets, and stagesProduce software solutions following best practices around release, deployment, and DevOps for NLP systemsUnderstand best practices, opportunities, and the roadmap for NLP from a business and product leader...s perspective Spis treści: 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