1. Program Chair: Tom Reamy Chief Knowledge Architect
Text Analytics Forum 2018
Program Chair: Tom Reamy Chief Knowledge Architect
KAPS Group
Author: Deep Text

2. Text Analytics Forum (TAF) Introduction Conference Highlights and Themes
Second year – following an inaugural success
KMWorld – TA is a means of enriching KM
Enriched content, expertise, collaboration
TBC – TA Minds the Gap between taxonomy and content
New knowledge organizations, cognitive-based
ESD – TA is best means of improving search
Faceted search, semi-automated subject tagging
SharePoint/ O365 – all major TA vendors integrate with it
Hybrid model – software characterizing document, sent to author/editor for human check

3. Text Analytics Forum (TAF) Introduction Conference Highlights and Themes
Overview of field of text analytics
Keynote Panel on Cognitive Computing
Two tracks – technical / applications
Technical
ML and Rules, TA and Cognitive Computing, New Techniques, Auto-tagging and Entity Extraction, Modeling Tacit Knowledge
Business / Applications
TA Basics, Business Case, Practical AI, Structure in Documents, Q&A systems, Multiple Case Studies – Space to Human Trafficking
Ask the Experts Panel
Some questions about the field of TA
We want your questions
Book Signing TU – 3:30 – 4:15, Wed – 3:15-4:00

4. Agenda Text Analytics Introduction What is it? What is it good for?
Key Ideas – Present and Future
Content Structure
Deep Learning and Symbolic AI
Obstacles in Text Analytics Adoption
Selling the Benefits – new approach
Questions

5. Text Analytics Forum (TAF) Introduction Deep Text: The Book – Who Am I?
KAPS Group – 13 years, Network of consultants (“hiring”)
Taxonomy to text analytics
Consulting, development – platform and applications
Strategy, Smart Start, Search, Smart Social Media
TA Training (1 day to 1 month), TA Audit
Partners – Synaptica, SAS, IBM, Expert System, Smartlogic, etc.
Clients: Genentech, Novartis, Northwestern Mutual Life, Financial Times, Hyatt, Home Depot, Harvard, British Parliament, Battelle, Amdocs, FDA, GAO, World Bank, Dept. of Transportation, IMF, etc.
Presentations, Articles, White Papers –

6. A treasure trove of technical detail, likely to become a definitive source on text analytics – Kirkus Reviews

7. Text Analytics Forum (TAF) Introduction What is Text Analytics?
Text analytics is the use of software and knowledge models to analyze/utilize poly-structured text.
Text Mining – NLP, statistical, predictive, machine learning
Different skills, mind set, Math & data not language
Annotation/Extraction – entities and facts – known and unknown, concepts, events - catalogs with variants, rule based
Sentiment Analysis
Entities and sentiment words – statistics & rules
Summarization
Dynamic – based on a search query term
Document – based on primary topics, position in document

8. Text Analytics Forum (TAF) Introduction What is Text Analytics?
Auto-categorization = the brains of the outfit
Training sets – Bayesian, Vector space
Terms – literal strings, stemming, dictionary of related terms
Boolean– Full search syntax – AND, OR, NOT
Advanced – DIST(#), ORDDIST#, PARAGRAPH, SENTENCE
Applications:
Search, BI, CI, Financial Services, eDiscovery, etc.
Fraud – Function word patterns / Fake News
Sentiment analysis – beyond positive and negative
Whole new applications – customers likely to cancel, new?
Platform for multiple features – Sentiment, Extraction
Disambiguation - Identification of objects, events, context
Fact Extraction – context around words, concepts

9. Text Analytics Forum (TAF) Introduction Trends
Market B to 10.5B 2023
Cloud Technology Growing
Real time analytics, text from anywhere
BOTs in the enterprise
Conversational Interfaces
Social – Behavioral Analytics
New models of people
New Knowledge Organizations
K Graphs
Emotion, Motivation Taxonomies
AI and ML – hype? Or ?

10. Text Analytics Forum (TAF) Introduction
Key Ideas / Trends in Text Analytics

11. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
Poly-structured text – Content Types and Sections
Deep Text a foundation for multiple applications
Using sections for better auto rules
AI / Machine Learning AND Rules
Obstacles for Text Analytics
New Approach to Selling the Benefits of Text Analytics

12. Text Analytics Forum (TAF) Introduction Adding Structure to Unstructured Content
Content Type – defined by sections
Blogs, Announcements, Articles, Press Releases, News, Case Reports, Correspondence
Sections
Metadata and text indicators – rules to find
Document Level: Title-Keywords, Abstract, summary, etc.
Special sections – Methods, Objectives, Results, etc.
Data patterns – dates, addresses – need context rules
Weights – ignore all but section text to sophisticated weighting
Clusters and machine learning – at section level, not document
Clusters as sections, clusters within sections
Worst method – strip out all indicators and use a Bag of Words

16. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
Poly-structured text – Content Types and Sections
AI / Machine Learning AND Rules
Data AND Concepts
Obstacles for Text Analytics
New Approach to Selling the Benefits of Text Analytics

17. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics: Deep Learning & ML
Neural Networks – from 1980’s
New = size and speed, larger networks = can learn better and faster
Machine Learning – Deep Learning but less and more
Limited granularity – high level categories, very orthogonal
Faster to get started and get to 60% - then the wall
Biggest advantage = ML can learn – connection strength
Strongest in areas like image recognition, physical patterns
Weakest – concepts, subjects, deep language, metaphors, etc.
Deep Learning is a Dead End - accuracy – 60-70% - concepts

18. Text Analytics Forum (TAF) Introduction Deep Text vs. Deep Learning
Black Box – Watson – “We don’t know how or why it works”
Susceptible to bias – hard to fix
Domain Specific, data not deep understanding
No common sense & no strategy to get there (faster not enough)
Do rules take more effort to develop?
Some studies show it is less
Rules-Based
Rule-based system reported 92 percent accuracy and a fourfold increase in productivity.
Less up front cost, and less time spent refining
Transparent – can fix, refine

19. Text Analytics Forum (TAF) Introduction Combine ML and Rules
Future = Combine machine learning and rules
Application Level to categorization language level
Cognitive Model – Thinking Fast and Slow
Automatic answers – Good until they are not
Slow – complex problem solving
Add content structure/ contexts – makes both smarter
Rules – right kind of rules stable logic, variable text
IF pattern > Threshold THEN X
Hierarchical Networks
Rules that learn

20. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
Poly-structured text – Content Types and Sections
AI / Machine Learning AND Rules
Obstacles for Text Analytics
Survey Says
New Approach to Selling the Benefits of Text Analytics

21. Text Analytics Forum (TAF) Introduction Obstacles to Text Analytics
Survey Says: Lack of Knowledge & Standards
Text is an order of magnitude more complex than data
Entities – Facts - Concepts
No SQL for text
Python is lower level language
Common functions, disparate syntax
Requires interdisciplinary – programming and language skills
Rare that organizations have this collaboration set up
No certification for TA development
Vendor specific training
Software is expensive and difficult

22. Text Analytics Forum (TAF) Introduction Obstacles to Text Analytics: Possible Solutions
Buy my book!
Develop and adopt standards
Simplify development process but keep power
Develop better training
Not just one week of software training
Better types of rules
Theoretical Breakthrough – ML AND Rules
Major Power takes over – Adobe, Microsoft, ?
Hybrid solutions – Apple and Goggle – 1,000’s of humans
Good, But still a hard sell

23. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
Poly-structured text – Content Types and Sections
AI / Machine Learning AND Rules
Obstacles for Text Analytics
New Approach to Selling the Benefits of Text Analytics
Seeing is Believing

24. Benefits of Text Analytics Selling the Benefits - Overview
Start with numerical studies - ROI
Stories – Pharma example
Stories – find own real life stories
Focus of Stories: Business Objectives
Selling to C Level
Different language
Need to educate – what it is and why
Internal Advocacy is Key
BUT – Fatal Flaw – Hard to believe!

25. Text Analytics Forum (TAF) Introduction Selling the Vision
New Approach – Mini-POC
One approach – One week
Elements
Taxonomy (Old, one branch) – nodes to 100
Sample content – documents per node
Simple content model – document sections
Build categorization rules for all nodes
Cat rules faster, easier, better based on sections
Demo – Simple search (15%-50%) to 90%+ (Story)

26. Text Analytics Forum (TAF) Introduction Benefits: Selling the Vision: Mini-POC
Something that people can see, touch, play with
Real application with real content
See the value of Taxonomy + Text Analytics
Appeal to all audiences – Librarians to KM to technology geeks to executives
Option – Comparison with fully automatic clusters
Start of building a foundation for full enterprise
Full POC can build (most of) that foundation

27. Text Analytics Forum (TAF) Introduction Conclusions
No such thing as unstructured text
Content Models / Sections capture and utilize structure
Power of AND
Still early – ML and rules
Wonderful mix of hype and reality
AI-Deep Learning – still “Two years away” for concepts
Data is here and getting smarter
Main obstacles are complexity of language and lack of knowledge
New Approach to educate and get buy in – Mini-POC
Enjoy the conference!

28. Questions? Tom Reamy tomr@kapsgroup.com KAPS Group
Knowledge Architecture Professional Services