کتاب پردازش زبان طبیعی کاربردی

مقدمه‌ای بر NLP

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

خط لوله NLP

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

نمایش متن

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

طبقه‌بندی متن

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

استخراج اطلاعات

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

چت‌بات‌ها

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 • Deep Dive into Components of a Dialog System • Dialog Act Classification • Identifying Slots • Response Generation • Dialog Examples with Code Walkthrough • 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

موضوعات کوتاه

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 • What’s 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

رسانه‌های اجتماعی

Applications • Unique Challenges • NLP for Social Data • Word Cloud • Tokenizer for SMTD • Trending Topics • Understanding Twitter Sentiment • Pre-Processing SMTD • Text Representation for SMTD • Customer Support on Social Channels • Memes and Fake News • Identifying Memes • Fake News • Wrapping Up

تجارت الکترونیک و خرده‌فروشی

E-Commerce Catalog • Review Analysis • Product Search • Product Recommendations • Search in E-Commerce • Building an E-Commerce Catalog • Attribute Extraction • Product Categorization and Taxonomy • Product Enrichment • Product Deduplication and Matching • Review Analysis • Sentiment Analysis • Aspect-Level Sentiment Analysis • Connecting Overall Ratings to Aspects • Understanding Aspects • Recommendations for E-Commerce • A Case Study: Substitutes and Complements • Wrapping Up

مراقبت‌های بهداشتی، مالی و حقوقی

Healthcare • Health and Medical Records • Patient Prioritization and Billing • Pharmacovigilance • Clinical Decision Support Systems • Health Assistants • Electronic Health Records • Mental Healthcare Monitoring • Medical Information Extraction and Analysis • Finance and Law • NLP Applications in Finance • NLP and the Legal Landscape • Wrapping Up

فرآیند کامل NLP

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 • 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