Practical natural language processing: a comprehensive guide to building real-world NLP systems
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 sci...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
June 2020
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Zusammenfassung: | 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. |
| Beschreibung: | xxvii, 424 Seiten Illustrationen, Diagramme |
| ISBN: | 9781492054054 |
Internformat
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Datensatz im Suchindex
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| adam_text | Table of Contents Foreword............................................................................................. xv Preface............................................................................................... xvii Part I. Foundations 1. NLP: A Primer............................................................................................ 3 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 5 6 8 9 12 14 16 16 19 22 28 31 33 2. NLP Pipeline............................................................................................37 Data Acquisition Text Extraction and Cleanup HTML Parsing and Cleanup Unicode Normalization Spelling Correction 39 42 44 45 46 vii
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 47 49 50 52 55 57 60 62 62 62 63 64 65 68 68 71 72 72 72 73 73 74 76 3. Text Representation................................................................................................. 81 Vector Space Models 84 Basic Vectorization Approaches 85 One-Hot Encoding 85 Bag of Words 87 Bag of N-Grams 89 TF-IDF 90 Distributed Representations 92 Word Embeddings 94 Going Beyond Words 103 Distributed Representations Beyond Words and Characters 105 Universal Text Representations 107 Visualizing Embeddings 108 Handcrafted Feature Representations 112 Wrapping Up 113 viii I Table of Contents
Part II. Essentials 4. Text Classification................................................................................. 119 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 121 123 125 126 126 127 131 132 134 134 136 138 140 143 144 145 147 148 149 149 150 152 155 157 5. Information Extraction............................................................................................161 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 162 164 165 166 167 168 169 171 175 176 177 178 179 Table of Contents | ix
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 181 182 184 185 186 187 189 190 193 6. Chatbots......................................................................................................................... 199 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 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 x j Table of Contents 200 201 202 204 204 205 206 208 218 219 219 220 221 226 227 227 228 229 232 233 235 236 241 243 245 248 249 251 252 256
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 Partili. 257 258 258 259 260 262 263 264 265 266 267 268 270 270 271 Applied Social Media............................................................... ........................277 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 279 280 286 286 288 288 290 292 296 299 301 301 302 304 E-Commerce and Retail.................................................. ...................... 309 E-Commerce Catalog Review Analysis Product Search Product Recommendations Search in E-Commerce Building an E-Commerce Catalog 310 310 311 311 311 314 Table of Contents | xi
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 314 319 323 325 326 327 329 331 332 334 335 338 10. Healthcare, Finance, and Law.................................................................. 341 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 341 343 344 344 344 344 346 355 357 360 362 365 368 Part IV. Bringing It All Together 11· The End-to-End NLP Process.................................................................... 373 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 xii I Table of Contents 374 376 378 379 380 381 381 384 385 386
The Data Science Process The KDD Process Microsoft Team Data Science Process Making AĪ 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. 390 390 392 394 394 395 396 397 398 400 403 409
Practical Natural Language Processing 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 Şurana 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 NLP • Implement and evaluate different NLP applications using machine learning and deep learning methods • Fine-tune your NLP solution based on your business problem and industry vertical • Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages • Produce software solutions following best practices around release, deployment, and DevOps for NLP systems • Understand best practices, opportunities, and the roadmap for NLP from a business and product leader s perspective
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| adam_txt |
Table of Contents Foreword. xv Preface. xvii Part I. Foundations 1. NLP: A Primer. 3 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 5 6 8 9 12 14 16 16 19 22 28 31 33 2. NLP Pipeline.37 Data Acquisition Text Extraction and Cleanup HTML Parsing and Cleanup Unicode Normalization Spelling Correction 39 42 44 45 46 vii
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 47 49 50 52 55 57 60 62 62 62 63 64 65 68 68 71 72 72 72 73 73 74 76 3. Text Representation. 81 Vector Space Models 84 Basic Vectorization Approaches 85 One-Hot Encoding 85 Bag of Words 87 Bag of N-Grams 89 TF-IDF 90 Distributed Representations 92 Word Embeddings 94 Going Beyond Words 103 Distributed Representations Beyond Words and Characters 105 Universal Text Representations 107 Visualizing Embeddings 108 Handcrafted Feature Representations 112 Wrapping Up 113 viii I Table of Contents
Part II. Essentials 4. Text Classification. 119 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 121 123 125 126 126 127 131 132 134 134 136 138 140 143 144 145 147 148 149 149 150 152 155 157 5. Information Extraction.161 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 162 164 165 166 167 168 169 171 175 176 177 178 179 Table of Contents | ix
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 181 182 184 185 186 187 189 190 193 6. Chatbots. 199 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 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 x j Table of Contents 200 201 202 204 204 205 206 208 218 219 219 220 221 226 227 227 228 229 232 233 235 236 241 243 245 248 249 251 252 256
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 Partili. 257 258 258 259 260 262 263 264 265 266 267 268 270 270 271 Applied Social Media. .277 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 279 280 286 286 288 288 290 292 296 299 301 301 302 304 E-Commerce and Retail. . 309 E-Commerce Catalog Review Analysis Product Search Product Recommendations Search in E-Commerce Building an E-Commerce Catalog 310 310 311 311 311 314 Table of Contents | xi
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 314 319 323 325 326 327 329 331 332 334 335 338 10. Healthcare, Finance, and Law. 341 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 341 343 344 344 344 344 346 355 357 360 362 365 368 Part IV. Bringing It All Together 11· The End-to-End NLP Process. 373 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 xii I Table of Contents 374 376 378 379 380 381 381 384 385 386
The Data Science Process The KDD Process Microsoft Team Data Science Process Making AĪ 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. 390 390 392 394 394 395 396 397 398 400 403 409
Practical Natural Language Processing 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 Şurana 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 NLP • Implement and evaluate different NLP applications using machine learning and deep learning methods • Fine-tune your NLP solution based on your business problem and industry vertical • Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages • Produce software solutions following best practices around release, deployment, and DevOps for NLP systems • Understand best practices, opportunities, and the roadmap for NLP from a business and product leader's perspective |
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| contents | Part 1. Foundations. NLP: a primer -- NLP pipeline -- Text representation -- Part 2. Essentials. Text classification -- Information extraction -- Chatbots -- Topics in brief -- Part 3. Applied. Social media -- E-commerce and retail -- Healthcare, finance, and law -- Part 4. Bringing it all together. The end-to-end NLP process |
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| discipline | Informatik Sprachwissenschaft Psychologie Literaturwissenschaft |
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| id | DE-604.BV046765793 |
| illustrated | Illustrated |
| index_date | 2024-07-03T14:45:19Z |
| indexdate | 2024-07-10T08:53:12Z |
| institution | BVB |
| isbn | 9781492054054 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032175288 |
| oclc_num | 1193293284 |
| open_access_boolean | |
| owner | DE-384 DE-1043 DE-29T DE-12 DE-83 DE-92 DE-M382 DE-523 DE-859 DE-1102 DE-739 |
| owner_facet | DE-384 DE-1043 DE-29T DE-12 DE-83 DE-92 DE-M382 DE-523 DE-859 DE-1102 DE-739 |
| physical | xxvii, 424 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | Vajjala, Sowmya Verfasser (DE-588)1074835425 aut Practical natural language processing a comprehensive guide to building real-world NLP systems Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana First edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly June 2020 xxvii, 424 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Part 1. Foundations. NLP: a primer -- NLP pipeline -- Text representation -- Part 2. Essentials. Text classification -- Information extraction -- Chatbots -- Topics in brief -- Part 3. Applied. Social media -- E-commerce and retail -- Healthcare, finance, and law -- Part 4. Bringing it all together. The end-to-end NLP process 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. Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Natural language processing (Computer science) Application software / Development Text data mining Machine learning Computerlinguistik (DE-588)4035843-4 s Sprachverarbeitung (DE-588)4116579-2 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Majumder, Bodhisattwa Verfasser (DE-588)1218318112 aut Gupta, Anuj 1964- Verfasser (DE-588)1146586035 aut Surana, Harshit Verfasser (DE-588)1218318414 aut Erscheint auch als Online-Ausgabe 978-1-4920-5402-3 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032175288&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032175288&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Vajjala, Sowmya Majumder, Bodhisattwa Gupta, Anuj 1964- Surana, Harshit Practical natural language processing a comprehensive guide to building real-world NLP systems Part 1. Foundations. NLP: a primer -- NLP pipeline -- Text representation -- Part 2. Essentials. Text classification -- Information extraction -- Chatbots -- Topics in brief -- Part 3. Applied. Social media -- E-commerce and retail -- Healthcare, finance, and law -- Part 4. Bringing it all together. The end-to-end NLP process Computerlinguistik (DE-588)4035843-4 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4035843-4 (DE-588)4116579-2 (DE-588)4434275-5 |
| title | Practical natural language processing a comprehensive guide to building real-world NLP systems |
| title_auth | Practical natural language processing a comprehensive guide to building real-world NLP systems |
| title_exact_search | Practical natural language processing a comprehensive guide to building real-world NLP systems |
| title_exact_search_txtP | Practical natural language processing a comprehensive guide to building real-world NLP systems |
| title_full | Practical natural language processing a comprehensive guide to building real-world NLP systems Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana |
| title_fullStr | Practical natural language processing a comprehensive guide to building real-world NLP systems Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana |
| title_full_unstemmed | Practical natural language processing a comprehensive guide to building real-world NLP systems Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana |
| title_short | Practical natural language processing |
| title_sort | practical natural language processing a comprehensive guide to building real world nlp systems |
| title_sub | a comprehensive guide to building real-world NLP systems |
| topic | Computerlinguistik (DE-588)4035843-4 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Computerlinguistik Sprachverarbeitung Python Programmiersprache |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032175288&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032175288&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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