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

2. Agenda Introduction – Welcome Text Analytics Introduction
Overview of Conference
Text Analytics Introduction
What is it?
What is it good for?
Results of TAF Survey
Key Ideas – Present and Future
Questions

3. Text Analytics Forum (TAF) Introduction Conference Highlights and Themes
Newest member of Info Today family of conferences
First year of many?
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 – all major TA vendors integrate with it
Hybrid model – software characterizing document, sent to author/editor for human check

4. Text Analytics Forum (TAF) Introduction Conference Highlights and Themes
Overview of field of text analytics
General and current market by Seth Grimes
Two tracks – technical / business & applications
Could be development and applications
Technical
AI and TA, Cognitive computing, graph databases, Text and Data
ML vs. Rules, taxonomy, Auto-categorization
Business / Applications
Search & TA, Fake News & Ads, TA and Taxonomy
Case Studies, New Applications, Issues in Applications
Ask the Experts Panel
Some questions about the field of TA
We want your questions

5. Text Analytics Forum (TAF) Introduction Deep Text: The Book – Who Am I?
Professional student / independent consultant – all but 6 years
History of Ideas to Programmer – AI (Only 2 years away)
Games – Galactic Gladiators/Adventures – still available
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, 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 structures in 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

9. Text Analytics Forum (TAF) Introduction What is Text Analytics Good For?
Just about anything textual you can think of
Enterprise:
Search, BI, CI, Financial Services, eDiscovery, etc.
Fraud – Function word patterns
Adding text (depth and intelligence) to all data-based applications
Whole new applications – customers likely to cancel, new?
Social:
Social Media analysis – adding text to data
Sentiment analysis – beyond positive and negative
Fake news – multiple module model

10. Text Analytics Forum (TAF) Introduction Future Directions: Survey Results – 2017
Important Areas:
Business Intelligence – 87%
Decision Support - 83%
Financial Intelligence – 81%
KM-Productivity – 80%
Search – Search Apps – 78%
Security – 77%
Compliance – 76%
Voice of Customer – 73%
Social Media Analysis – 69%

11. Text Analytics Forum (TAF) Introduction Future Directions: Survey Results – 2017
Who is driving TA?
R&D – 25%
IT – 22%
Rest are minor
Factors slowing adoption of TA
Lack of Knowledge/value – 43%
Financial – 18%
Lack of in-house expertise – 11%
What new capabilities?
Deep Learning, ML, AI – 23%

12. Text Analytics Forum (TAF) Introduction Future Directions: Survey Results – 2017
What do you like about TA software?
Ease of Use
Configurability
Accuracy, quality of results
What don’t you like?
Difficult
No one solution – domain specific
Most difficult aspect of TA initiatives?
Data Preparation
Language complexity
Understanding business needs, domain resources

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

14. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
AI / Deep Learning to the Rescue?
Humans obsolete or empowered?
Machine Learning vs. Rules-based
Poly-structured text – Content Types and Sections
New Knowledge Structures – Cognitive & Social

15. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics: Deep Learning
Neural Networks – from 1980’s
New = size and speed
Larger networks = can learn better and faster
Multiple networks = more “intelligence” – networks output fed to other networks
Strongest in areas like image recognition, physical patterns
Weakest – concepts, subjects, deep language, metaphors, etc.

16. Text Analytics Forum (TAF) Introduction Deep Text vs. Deep Learning
Deep Learning is a Dead End - accuracy – 60-70%
Black Box – don’t know how to improve except indirect manipulation of input
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 (things fall, don’t wink in and out of existence
No strategy to get there (faster not enough)
Major – loss of quality – who is training who?
Project personality and intelligence – on everything!
Extra Benefits of a Deep Text Approach – Multiple InfoApps

17. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics: Automatic Taxonomy
Most Text Analytics vendors offered – very poor results, dropped
New techniques – getting better but don’t give up your taxonomist day job
Automatic – but not a taxonomy – cluster of co-occurring terms
Suggest terms and relationships
Text mining on steroids
“Automatic” – huge human effort to design approach, mathematics, select content, seed taxonomies, keyword selection, data prep – then voila!

18. AI and Taxonomy AI: Past and Present

19. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
AI / Deep Learning to the Rescue?
Machine Learning vs. Rules-based
Right kind of rules – general structure
Learning with rules, ML with structure
Poly-structured text – Content Types and Sections
New Knowledge Structures – Cognitive & Social

20. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
Machine Learning – Deep Learning but less
Limited granularity – high level categories, very orthogonal
Faster to get started and get to 60% - then the wall
ML – scale – can do millions of texts
But – both require upfront development – and once done, both can handle the same amount of content
Do rules take more effort to develop?
Some studies show it is less: “A rule-based system recoups its value in one month, compared with almost five years under the statistics-based approach”

21. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
Rules-Based
Rule-based system reported 92 percent accuracy and a fourfold increase in productivity.
Less up front cost, and less time spent refining
Statistical Approach
Maximum accuracy achieved, 72 percent; productivity doubled.
Why do IT departments favor ML?
ML uses programmers and statisticians – more of them available than librarians, taxonomists, metadata, puzzle people
Future = Combine machine learning and rules
Application Level to categorization language level

22. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
AI / Deep Learning to the Rescue?
Machine Learning vs. Rules-based
Poly-structured text – Content Types and Sections
Deep Text a foundation for multiple applications
Using sections for better auto rules
New Knowledge Structures – Cognitive & Social

23. 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

25. Text Analytics Forum (TAF) Introduction Key Ideas in Text Analytics
AI / Deep Learning to the Rescue?
Machine Learning vs. Rules-based
Poly-structured text – Content Types and Sections
New Knowledge Structures – Cognitive & Social
Relational Frame Theory
Deep Psychology/marketing

26. Text Analytics Forum (TAF) Introduction New Knowledge Structures
Multiple types of Knowledge Organization
Taxonomy – concepts, hierarchical
Ontology – any type of relationship, things and concepts
Knowledge Graphs – triples, unlimited, no overall structure, best for facts
K Graphs and hierarchical – best way to merge?
Modules, facets
Hierarchical network models
New types – cognitive science - RFT, other?
Brain is more than a network – universal language detector
Child at 6-9 months – tell the difference between words – forwards and backwards – in any language

27. Text Analytics Forum (TAF) Introduction AI and Taxonomy
Relational Frame Theory - RFT
Coordination – (similarity) dog is same as hound – types of similarity? Taxonomy of similarities?
Distinction – (difference) – white dog different than a black dog
Opposition – a black dog versus a while cat
Comparison – this dog is bigger than that dog
Spatial – this dog is on the left
Temporal – I fed the dog before the cat
Hierarchical – a dog is a sort of mammal
Causal – a dog bit causes me to cry

28. Text Analytics Forum (TAF) Introduction Conclusions
AI-Deep Learning – still “Two years away”
Deep Text
Linguistic and cognitive depth – human-like learning
Integration of multiple techniques and modules
Infrastructure – Move fast with a stable infrastructure
Enjoy the conference!
Stay tuned – Next Year
TAF season II – better than ever!
New generation of text analytics software?

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