book

SAS Text Analytics for Business Applications

Name: SAS Text Analytics for Business Applications
ISBN: 9781635266610

by Teresa Jade, Biljana Belamaric-Wilsey, Michael Wallis

March 2019

Beginner to intermediate

308 pages

9h 33m

English

SAS Institute

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About This Book
What Does This Book Cover?Is This Book for You?What Should You Know about the Examples?Software Used to Develop the Book’s ContentExample Code and DataWe Want to Hear from You
Acknowledgments
Chapter 1: Fundamentals of Information Extraction with SAS
1.1. Introduction to Information Extraction1.1.1. History1.1.2. Evaluation1.1.3. Information Extraction versus Data Extraction versus Information Retrieval1.1.4. Situations in Which to Use IE for Business Problems1.2. The SAS IE Toolkit1.2.1. NLP Foundation for IE1.2.2. LITI Rule Syntax1.2.3. Predefined Concepts1.2.4. Taxonomy of Concepts1.2.5. Algorithms for Matching1.2.6. Interfaces for Building and Applying Models1.3. Reasons for Using SAS IE1.4. When You Should Use Other Approaches instead of SAS IE1.5. Important Terms in the Book1.5.1. Strings versus Tokens1.5.2. Named Entities and Predefined Concepts1.5.3. Parent Forms and Other Variants1.5.4. Found Text and Extracted Match1.6. Suggested Reading
Chapter 2: Fundamentals of Named Entities
2.1. Introduction to Named Entities2.2. Business Scenarios2.2.1. Example: Pinpointing Location Information 2.2.2. Example: Identifying Supporters and Competitors 2.2.3. Example: Estimating Loss, Gain, and Risk2.2.4. Example: Detecting Personally Identifiable Information2.3. The SAS Approach2.3.1. Understanding Standard Predefined Concepts2.3.2. Understanding Underlying Principles2.3.3. Accessing the Predefined Concepts
Chapter 3: SAS Predefined Concepts: Enamex
3.1. Introduction to SAS Predefined Concepts3.2. Person3.2.1. Titles in Person Names3.2.2. Suffixes as Part of a Personal Name3.2.3. Single-Word Names3.2.4. Body References3.2.5. Quotes3.2.6. Locations as Part of Name3.2.7. Groups of Individuals3.2.8. Historical Figures, Saints, and Deities3.2.9. Animals, Fictional Characters, Artificial Intelligence, and Aliens3.2.10. Businesses Named after People3.2.11. Laws, Diseases, Prizes, and Works of Art3.3. Place3.3.1. Common Nouns and Determiners3.3.2. Subnational Regions and Other Descriptors3.3.3. Street Addresses3.3.4. Monuments3.3.5. Celestial Bodies3.3.6. Neighborhoods3.3.7. Fictional Place Names3.3.8. Conjoined Location Names3.3.9. Special Cases for Nonmatches3.4. Organization3.4.1. Corporate Designators or Suffixes3.4.2. Determiners before Proper Names3.4.3. Facility Names Associated with an Organization3.4.4. Groups of Individuals3.4.5. Aliases3.4.6. Conjoined Organization Names3.4.7. Event Names 3.4.8. Special Cases for Nonmatches3.5. Disambiguation of Matches3.5.1. Organization or Place3.5.2. Organization or Product3.5.3. Organization or Person
Chapter 4: SAS Predefined Concepts: Timex, Numex, and Noun Group
4.1. Introduction to Other SAS Predefined Concepts4.2. Date4.2.1 Extended ISO 8601 Format4.2.2. Named Dates4.2.3. Modifiers4.2.4. Conjoined Dates4.2.5. Duration4.2.6. Vague Expressions4.3. Time4.3.1. Extended ISO 8601 Format 4.3.2. Named Times and Time Zones4.3.3. Modifiers4.3.4. Conjoined Times4.3.5. Duration4.3.6. Vague Expressions4.4. Money4.4.1. Modifiers4.4.2. Rates and Ratios4.4.3. Quotes and Parentheses4.4.4. Conjoined Expressions4.4.5. Approximate Amount4.4.6. Expressions and Metaphors4.5. Percent4.5.1. Acronyms, Initialisms, and Abbreviations4.5.2. Modifiers4.5.3. Quotation Marks and Parentheses4.5.4. Conjoined Expressions4.5.5. Multiword Expressions4.5.6. Fractions and Ratios4.5.7. Special Cases for Nonmatches4.6. Noun Group4.7. Disambiguation of Matches4.8. Supplementing Predefined Concepts
Chapter 5: Fundamentals of Creating Custom Concepts
5.1. Introduction to Custom Concepts5.2. LITI Rule Fundamentals5.2.1. Required Parts of LITI Rules5.2.2. Optional Parts of LITI Rules5.2.3. Rule Definition5.3. Custom Concept Fundamentals 5.3.1. Best Practices for Naming Custom Concepts5.3.2. Best Practices for Referencing Custom Concepts5.3.3. Concepts versus CONCEPT and CONCEPT_RULE Rule Types5.3.4. Programmatic Rule Writing and Model Compilation5.3.5. Programmatic Model Application5.4. Troubleshooting All Rule Types
Chapter 6: Concept Rule Types
6.1. Introduction to the Concept Rule Types6.2. CLASSIFIER Rule Type6.2.1. Basic Use6.2.2. Advanced Use: Coreference Command6.2.3. Advanced Use: Information Field6.2.4. Troubleshooting6.2.5. Best Practices6.2.6. Summary6.3. CONCEPT Rule Type6.3.1. Basic Use6.3.2. Advanced Use: Combination of Various Elements6.3.3. Advanced Use: Combination of Elements and Modifiers6.3.4. Troubleshooting6.3.5. Best Practices6.3.6. Summary6.4. C_CONCEPT Rule Type6.4.1. Basic Use6.4.2. Advanced Use: Multiple Strings as Matches6.4.3. Advanced Use: Coreference6.4.4. Troubleshooting6.4.5. Best Practices6.4.5. Summary
Chapter 7: CONCEPT_RULE Type
7.1. Introduction to the CONCEPT_RULE Type7.2. Basic Use7.3. Advanced Use: Multiple and Embedded Operators7.4. Advanced Use: Negation Using NOT7.5. Advanced Use: Negation Using UNLESS7.6. Advanced Use: Coreference and Aliases7.7. Troubleshooting7.8. Best Practices7.9. Summary
Chapter 8: Fact Rule Types
8.1. Introduction to Fact Rule Types8.2. SEQUENCE Rule Type8.2.1. Basic Use8.2.2. Advanced Use with Other Elements8.2.3. Troubleshooting8.2.4. Best Practices8.2.5. Summary8.3. PREDICATE_RULE Rule Type8.3.1. Basic Use8.3.2. Advanced Use: Capture of a Sentence8.3.3. Advanced Use: More Complex Rules8.3.4. Advanced Use: Single Label, Multiple Extracted Matches 8.3.5. Advanced Use: More Than Two Returned Arguments 8.3.6. Advanced Use: Discovery of Terms to Add to a Model8.3.7. Troubleshooting8.3.8. Best Practices8.3.9. Summary

Chapter 9: Filter Rule Types
9.1. Introduction to Filter Rule Types9.2. REMOVE_ITEM Rule Type9.2.1. Basic Use of the REMOVE_ITEM Rule Type9.2.2. Advanced Use of REMOVE_ITEM: Additional Elements9.2.3. Advanced Use of REMOVE_ITEM: Negation9.2.3. REMOVE_ITEM Troubleshooting9.2.4. REMOVE_ITEM Best Practices9.2.5. REMOVE_ITEM Summary9.3. NO_BREAK Rule Type9.3.1. Basic Use of the NO_BREAK Rule Type9.3.2. Advanced Use of NO_BREAK: Specifying a Concept Name9.3.3. NO_BREAK Troubleshooting9.3.4. NO_BREAK Best Practices9.3.5. NO_BREAK Summary
Chapter 10: REGEX Rule Type
10.1. Introduction to the REGEX Rule Type10.2. Basic Use10.3. Advanced Use: Discovery of Patterns10.4. Advanced Use: Exploration10.5. Advanced Use: Identification of Tokens for Splitting in Post-processing10.6. Advanced Use: Information Field10.7. Troubleshooting REGEX10.8. Best Practices for Using REGEX10.9. Summary of REGEX
Chapter 11: Best Practices for Custom Concepts
11.1. Introduction to Boolean and Proximity Operators11.2. Best Practices for Using Operators11.2.1. Behavior of Groupings of Single Operators11.2.2. SAS Categorization Operators11.2.3. Combinations of Operators and Restrictions11.3. Best Practices for Selecting Rule Types11.3.1. Rule Types and Associated Computational Costs 11.3.2. Use of the Least Costly Rule Type for Best Performance 11.3.3. When Not to Use Certain Rule Types 11.3. Concept Rules in Models
Chapter 12: Fundamentals of Data Considerations
12.1. Introduction to Projects12.2. Data Considerations12.3. Data Evaluation12.4. Data Exploration12.5. Data Analysis12.5.1. Vocabulary Diversity12.5.2. Information Density12.5.3. Language Formality12.5.4. Information Complexity12.5.5. Domain Specificity12.6. Business Goals and Targeted Information12.7. Suggested Reading
Chapter 13: Fundamentals of Project Design
13.1. Introduction to Project Design13.2. Definition of Targeted Information 13.3. Taxonomy Design 13.3.1. Decomposition13.3.2. Concept Types13.4. Project Settings13.4.1. Match Algorithm and Priority13.4.2. Case Sensitivity13.5. Suggested Reading
Chapter 14: Fundamentals of Model Measurement
14.1. Introduction to Model Measurement14.2. Use of a Gold Standard Corpus14.3. Setup of a Gold Standard Corpus14.4. Setup of Approximate Annotations14.5. Creation of Samples for Development and Testing14.6. Model Quality and Decisions14.6.1. Strategies for Overcoming Low Recall14.6.2. Strategies for Overcoming Low Precision14.7. Model Monitoring14.8. Suggested Reading
References

Overview

Extract actionable insights from text and unstructured data.

Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics.

Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data.

Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS Visual Text Analytics, SAS Contextual Analysis, and SAS Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise.

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