Data Extraction for Structured Documents (04.02.2.23)
Content
Introduction
Files to Download
Structured vs Unstructured Documents sample
Data Extraction for Structured Documents - Quiz 1
Data Context
Data Context sample
Data Context in Grooper
Data Extraction for Structured Documents - Quiz 2
Collation Methods
Key-Value Pair: The Basics
Key-Value Pair: Additional Considerations
Key-Value List
Arrays And Ordered Arrays
Combine
Split
The Rest Of Them
Data Extraction for Structured Documents - Quiz 3
Data Modeling
Intro To A Data Model & Data Elements
Extraction Logic
Data Extraction for Structured Documents - Quiz 4
Generic Extractors
Date Extractor: Building Your First Extractor
Time Extractor: Value Variability
OMR Time: Intro to OMR Extraction
Generic Text Segment: The Most Generic Extractor
Label Segments And Value Segments: From Generic to Semi-Generic
Data Extraction for Structured Documents - Quiz 5
Field Extraction
Report Number: Intro to the Labeled Value Extractor
Crash Date: Exercise
City: Using Segment Extractors and Leveraging Output Groups
County: Intro to Default Values and Data Element Overrides
State: Intro to Expression Based Extraction
Crash Type: Exercise (And More...)
The Waterfall Technique: Sorting Extraction Results
Data Extraction for Structured Documents - Quiz 6
Data Sections
Intro to Data Sections & The Divider Method
Report Totals Section: Exercise
Multi-Instance Sections & The Simple Method
Other Data Section Extraction Methods
Data Extraction for Structured Documents - Quiz 7
Practical Example: The Party Info Section
Getting Started: Building a Section Extractor for a Single Document
Fleshing it Out: A Section Extractor for the Whole Document Set
Multi-Instance Data Field Extraction
Party Info: Last Name and First Name
Data Sections as an Organizational Tool & The VIN Data Field
Make and Model: Exercise
Plate Number and Plate State: Resolving Ambiguous Labels
Data Extraction for Structured Documents - Quiz 8
Finale
Finale
Data Extraction For Structured Documents - Course Survey
Data Extraction for Structured Documents - Exam Assessment
Data Extraction for Structured Documents - Lab Assessment
Completion rules
All units must be completed
Data Context
Data without context is meaningless. This video introduces the concept of "data context" as it applies to extracting data from your
documents. How are you going to build extractors in Grooper? How do you, as a human, find information on a page? Context is key to understanding and finding the
data you want to collect.
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