1. Intelligent Interactions with Search Results Getting Beyond Those Blue Results Lists (or Smart Text)
Tom Reamy Chief Knowledge Architect
KAPS Group
Program Chair – Text Analytics World
Taxonomy Boot Camp, KMWorld, Enterprise Search Summit: Nov. Washington DC

2. Agenda Case Study Beyond Search – Building on the Foundation
Introduction: Search and Structure: Smart Text
Smart Text– foundation of text analytics
Adding Structure to Unstructured Text
Dynamic Sections and more, Better Relevancy Calculations
Complex Document Summaries, Deeper Personalization
Case Study
Publishing: Processing 700K Proposals
Beyond Search – Building on the Foundation
Conclusions

3. KAPS Group: General Clients:
Knowledge Architecture Professional Services – Network of Consultants
Strategy – IM & KM - Text Analytics, Social Media, Integration
Services:
Taxonomy/Text Analytics development, consulting, customization
Text Analytics Fast Start – Audit, Evaluation, Pilot
Social Media: Text based applications – design & development
Clients:
Genentech, Novartis, Northwestern Mutual Life, Financial Times, Hyatt, Home Depot, Harvard Business Library, British Parliament, Battelle, Amdocs, FDA, GAO, DOT, World Bank, etc.
Partners – Expert System, SAS, SAP, IBM, FAST, Smart Logic, Concept Searching, Attensity, Clarabridge, Lexalytics,
Applied Theory – Faceted taxonomies, complexity theory, natural categories, emotion taxonomies
Presentations, Articles, White Papers –

4. Introduction: Elements of Smart Text - Text Analytics
Text Mining – NLP, statistical, predictive, machine learning
Extraction – entities – known and unknown, concepts, events
Disambiguation - Ford
Fact Extraction - ontology, relationships of entities
Sentiment Analysis - Positive Negative – products, companies,
Auto-categorization
Training sets, Terms
Rules – simple – position in text (Title, body, url)
Boolean– Full search syntax – AND, OR, NOT
Advanced – DIST(#), ORDDIST#, PARAGRAPH, SENTENCE
Based on taxonomy/ontology

5. Enterprise Text Analytics
Search is still #1 = 30-50% of applications
New Standard Search – facets (more and more metadata), auto-categorization built on taxonomies, clustering
Trend = Text Analytics/Search as Semantic Infrastructure
Platform for Info Apps (Search-based applications)
SharePoint – Major focus of TA companies – fix problems with taxonomy/folksonomy
Hybrid workflow – Publish document -> TA analysis -> suggestions for categorization, entities, metadata -> present to author
External information = more automation, extraction – precision more important

6. Enterprise Text Analytics Adding Structure to Unstructured Content
Beyond Documents – categorization by corpus, by page, sections or even sentence or phrase
Documents are not unstructured – variety of structures
Text indicators to define sections of the document
Objectives, Abstract, Purpose, Aim – all the “same” section
Sections – Specific - “Abstract” to Function “Evidence”
Start of section is easy – where does it end?
Experiment – clusters / vocabulary to define section
Textual complexity, level of generality

7. Enterprise Text Analytics Categorization and Beyond
Need to develop flexible categorization and taxonomy – tweets to 200 page PDF
Rules or sample documents?
Need more precision and granularity than documents can do
Training sets – not as easy as thought
Applications require sophisticated rules, not just categorization by similarity
Separate logic of the rules from the text
Stable rules, changing text
Scores – relevancy with thresholds
Not just frequency of words

9. Enterprise Text Analytics Document Type Rules
(START_2000, (AND, (OR, _/article:"[Abstract]", _/article:"[Methods]“), (OR,_/article:"clinical trial*", _/article:"humans",
(NOT, (DIST_5, (OR,_/article:"approved", _/article:"safe", _/article:"use", _/article:"animals"),
If the article has sections like Abstract or Methods
AND has phrases around “clinical trials / Humans” and not words like “animals” within 5 words of “clinical trial” words – count it and add up a relevancy score
Primary issue – major mentions, not every mention
Combination of noun phrase extraction and categorization
Results – virtually 100%

10. Case Study Publishing Project: Reed Construction Data
700,000 Proposals – Wide Variation
Process Proposals – extract data – types
Current Manual Process – Internal Teams
Expensive and Slow
Structure Variety of Unstructured Documents
Generate Table of Contents
Generate Sections and Capture Text
Semi-automatic extract Key Information
Save Time & Money, Flexible Hiring, New Offerings

11. Publishing Project: Example Rules Automated Table of Content

13. Publishing Project: Example Rules Key Data Extraction
Bid Dates/Times
Roles (Architect, Designer, etc.) – names and addresses, etc.
Project Attributes – Cost, Invitation Number, Parking, etc.
Some Easy, Some Hard – Address!
Example:
ARCHITECT: MICHEAL KIM ARCHITECTURE
1 HOLDEN STREET BROOKLINE, MA 02445
P: (617) F: (772)
Technique – create broad and stable templates, variation in the text

16. Publishing Project: Example Rules Key Project Data

17. Publishing Project: Process & Approach

18. Smart Search: Metadata, Metadata, Metadata
Basic Facets: Date, People, Organization, Content-Type
Advanced Facets: Materials, Methods, Project Attributes, etc.
Context dependent
Deep personalization
Selection of facets by role, community, task, content
Smart Summarization
Better conceptual description
Complex summaries – key data, document sections, etc.
Smart Search – beyond simple relevancy
Next – Beyond Search - active agents – don’t need questions

19. Building on the Foundation: Applications Pronoun Analysis: Fraud Detection; Enron Emails
Patterns of “Function” words reveal wide range of insights
Function words = pronouns, articles, prepositions, conjunctions, etc.
Used at a high rate, short and hard to detect, very social, processed in the brain differently than content words
Areas: sex, age, power-status, personality – individuals and groups
Lying / Fraud detection: Documents with lies have:
Fewer, shorter words, fewer conjunctions, more positive emotion words
More use of “if, any, those, he, she, they, you”, less “I”
Current research – 76% accuracy in some context

20. Building on the Foundation: Social Media Beyond Simple Sentiment
Beyond Good and Evil (positive and negative)
Degrees of intensity, complexity of emotions and documents
Importance of Context – around positive and negative words
Rhetorical reversals – “I was expecting to love it”
Issues of sarcasm, (“Really Great Product”), slanguage
Essential – need full categorization and concept extraction
New Taxonomies – Appraisal Groups – “not very good”
Supports more subtle distinctions than positive or negative
Emotion taxonomies - Joy, Sadness, Fear, Anger, Surprise, Disgust
New Complex – pride, shame, confusion, skepticism

21. Building on the Foundation: Applications Behavior Prediction – Telecom Customer Service
Problem – distinguish customers likely to cancel from mere threats
Basic Rule / Intention
(START_20, (AND,  (DIST_7,"[cancel]", "[cancel-what-cust]"),
(NOT,(DIST_10, "[cancel]", (OR, "[one-line]", "[restore]", “[if]”)))))
Examples:
customer called to say he will cancell his account if the does not stop receiving a call from the ad agency.
cci and is upset that he has the asl charge and wants it off or her is going to cancel his act
More sophisticated analysis of text and context in text
Combine text analytics with Predictive Analytics and traditional behavior monitoring for new applications

22. Building on the Foundation: Current Applications
Survey Analysis – Add analysis of free text
Automated Essay Scoring – Second Generation
Beyond words (polysyllabic) to meaning
Story Telling – Data Heavy, Sports, Finance
90% of news machine written by 2025, books?
Legal Review / eDiscovery
TA- categorize and filter to smaller, more relevant set
Payoff is big – One firm with 1.6 M docs – saved $2M
Voice of the Customer / Employee / Voter
Analysis of Blogs, Tweets, Social Networks
Early Identify problems with products and services
Customer Relationship & Brand Management, Fraud Detection

23. Smart Text : New Directions - Integration Deep Integration – Text Analytics
New Forms of Rules – Combine Text Mining and Text Analytics
Incorporate clusters – CLUSTER Operator
Like SENTENCE but more flexible, dynamic
More Dynamic Sections
Build up from “Categorization” of sentences – based on co-reference
Smaller units – Appraisal Taxonomies for Subjects, Build Larger Units
Complex Units – Collections of Paragraphs based on meaning
Sentence Level Sentiment Techniques for Subjects
Smarter Relevancy – not frequency – develop new scoring
Hybrid Machine-Human
Where, When, and How
Development, Tagging, Usage, Analytics, etc.
How Get Best of Both?

24. Conclusions
Text Analytics can feed/extend Big Data and Cognitive Science applications
Discover structure in (un)structured text
Apply text analytics to sections of document – new kinds of relevancy
Creating multiple views into data inside text – smart search results – interactive (facets plus)
Modular design – better search, new applications, Watson
Future: Cognitive Computing:
Learns, discover patterns based on context, highly integrated, meaning-based, highly interactive
Text Analytics adds depth of meaning
Future – Women, Fire, and Dangerous Things
Text Analytics and Cognitive Science = Metaphor Analysis, deep language understanding, common sense?

25. Coming Soon!
New Book coming: Text Analytics: Everything You Need to Know to Conquer Information Overload, Mine Social Media for Real Value, and Turn Big Text into Big Data
November

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