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Text Analytics World Future Directions of Text Analytics. Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com. Agenda. Introduction: Current State of Text Analytics Survey Roadblocks for Text Analytics

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text analytics world future directions of text analytics

Text Analytics World Future Directions of Text Analytics

Tom ReamyChief Knowledge Architect

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

agenda
Agenda
  • Introduction:
    • Current State of Text Analytics
    • Survey
  • Roadblocks for Text Analytics
    • Complexity and Customization
  • Fast and Slow (Thinking) Text Analytics
    • Building Text Analytics Brains
  • New Methods for Text Analytics
    • Lessons from Watson
    • Some Wild New Ideas and Approaches
  • Questions
introduction kaps group
Introduction: KAPS Group
  • Knowledge Architecture Professional Services – Network of Consultants
  • Applied Theory – Faceted taxonomies, complexity theory, natural categories, emotion taxonomies
  • Services:
    • Strategy – IM & KM - Text Analytics, Social Media, Integration
    • Taxonomy/Text Analytics development, consulting, customization
    • Text Analytics Quick Start – Audit, Evaluation, Pilot
    • Social Media: Text based applications – design & development
  • Partners – SAS, Smart Logic, Expert Systems, SAP, IBM, FAST, Concept Searching, Attensity, Clarabridge, Lexalytics
  • Projects – Portals, taxonomy, Text analytics – news, expertise location, information strategy, text analytics evaluation, Quick Start in Text A.
  • Clients: Genentech, Novartis, Northwestern Mutual Life, Financial Times, Hyatt, Home Depot, Harvard Business Library, British Parliament, Battelle, Amdocs, FDA, GAO, World Bank, etc.
  • Presentations, Articles, White Papers – www.kapsgroup.com
introduction what is text analytics
Introduction:What is Text Analytics?
  • Text Mining – NLP, statistical, predictive, machine learning
  • Semantic Technology – ontology, fact extraction
  • Extraction – entities – known and unknown, concepts, events
    • Catalogs with variants, rule based
  • Sentiment Analysis
    • Objects and phrases – statistics & rules – Positive and Negative
  • Auto-categorization
    • Training sets, Terms, Semantic Networks
    • Rules: Boolean - AND, OR, NOT
    • Advanced – DIST(#), ORDDIST#, PARAGRAPH, SENTENCE
    • Disambiguation - Identification of objects, events, context
    • Build rules based, not simply Bag of Individual Words
text analytics world current state of text analytics
Text Analytics WorldCurrent State of Text Analytics
  • History – academic research, focus on NLP
  • Inxight –out of ZeroxParc
    • Moved TA from academic and NLP to auto-categorization, entity extraction, and Search-Meta Data
  • Explosion of companies – many based on Inxight extraction with some analytical-visualization front ends
    • Half from 2008 are gone - Lucky ones got bought
  • Early applications – News aggregation and Enterprise Search –
  • Second Wave = shift to sentiment analysis
  • Enterprise search down, taxonomy up –need for metadata – not great results from either – 10 years of effort for what?
  • Text Analytics is growing – But
text analytics world current state of text analytics1
Text Analytics WorldCurrent State of Text Analytics
  • Current Market: 2012 – exceed $1 Bil for text analytics (10% of total Analytics)
  • Growing 20% a year
  • Search is 33% of total market
  • Other major areas:
    • Sentiment and Social Media Analysis, Customer Intelligence
    • Business Intelligence, Range of text based applications
  • Fragmented market place – full platform, low level, specialty
    • Embedded in content management, search, No clear leader.
text analytics world current state of text analytics vendor space
Text Analytics WorldCurrent State of Text Analytics: Vendor Space
  • Taxonomy Management – SchemaLogic, Pool Party
  • From Taxonomy to Text Analytics
    • Data Harmony, Multi-Tes
  • Extraction and Analytics
    • Linguamatics (Pharma), Temis, whole range of companies
  • Business Intelligence – Clear Forest, Inxight
  • Sentiment Analysis – Attensity, Lexalytics, Clarabridge
  • Open Source – GATE
  • Stand alone text analytics platforms – IBM, SAS, SAP, Smart Logic, Expert System, Basis, Open Text, Megaputer, Temis, Concept Searching
  • Embedded in Content Management, Search
    • Autonomy, FAST, Endeca, Exalead, etc.
future directions survey results
Future Directions: Survey Results
  • 28% just getting started, 11% not yet
  • What factors are holding back adoption of TA?
    • Lack of clarity about value of TA – 23.4%
    • Lack of knowledge about TA – 17.0%
    • Lack of senior management buy-in - 8.5%
    • Don’t believe TA has enough business value -6.4%
  • Other factors
    • Financial Constraints – 14.9%
    • Other priorities more important – 12.8%
  • Lack of articulated strategic vision – by vendors, consultants, advocates, etc.
text analytics world primary obstacle complexity
Text Analytics WorldPrimary Obstacle: Complexity
  • Usability of software is one element
  • More important is difficulty of models:
    • Conceptual and document models
  • General need – more structure but also more flexible kinds of structure and interactions
  • More modules and more ways of combining or interacting – IBM – select best answer but others
    • Competitive – learn and evolve – Feedback!
    • Cooperative – join together to form higher level structures
text analytics world primary obstacle complexity partial solutions
Text Analytics WorldPrimary Obstacle: Complexity: Partial Solutions
  • Build complex semantic networks – basic concepts – good for demo, gets a start, but very complex to build on
  • Library of taxonomies – but all need major customization and often are not a good starting point – different types of taxonomies – index vs. categorization
  • Customization – Text Analytics– heavily context dependent
    • Content, Questions, Taxonomy-Ontology
    • Level of specificity – Telecommunications
    • Specialized vocabularies, acronyms
    • Specialized relationships – conceptual and organizational
    • How overcome?
text analytics world thinking fast and slow daniel kahneman
Text Analytics World Thinking Fast and Slow – Daniel Kahneman
  • System 1 and System 2 – Daniel Kahneman
  • System 1 – fast and automatic – little conscious control
  • Represents categories as prototypes – stereotypes
    • Norms for immediate detection of anomalies – distinguish the surprising from the normal
    • fast detection of simple differences, detect hostility in a voice, find best chess move (if a master)
    • Priming / Anchoring – susceptible to systemic errors
      • Temperature Example
    • Biased to believe and confirm
    • Focuses on existing evidence (ignores missing – WYSIATI)
  • .
text analytics world thinking fast and slow
Text Analytics World Thinking Fast and Slow
  • System 2 – Complex, effortful judgments and calculations
    • System 2 is the only one that can follow rules, compare objects on several attributes, and make deliberate choices
    • Understand complex sentences
    • Check the validity of a complex logical argument
    • Focus attention – can make people blind to all else – Invisible Gorilla
  • Similar to traditional dichotomies – Tacit – Explicit, etc
  • Basic Design – System 1 is basic to most experiences, and System 2 takes over when things get difficult – conscious control
  • Text Analysis and Text Mining / Auto-Cat and TA Cat
text analytics world system 1 2 and text analytics approaches
Text Analytics WorldSystem 1 & 2 – and Text Analytics Approaches
  • “Automatic Categorization” – System 1 prototypes
    • Limited value -- only works in simple environments
    • Shallow categories with large differences
    • Not open to conscious control
  • System 2 – categories – complex, minute differences, deep categories
  • Together:
    • Choose one or other for some contexts
    • Combine both – need to develop new kinds of categories and/or new ways to combine?
text analytics world text mining and text analytics
Text Analytics World Text Mining and Text Analytics
  • Text Analytics and Big Data enrich each other
    • Data tells you what people did, TA tells you why
  • Text Analytics – pre-processing for TM
    • Discover additional structure in unstructured text
    • Behavior Prediction – adding depth in individual documents
    • New variables for Predictive Analytics, Social Media Analytics
    • New dimensions – 90% of information, 50% using Twitter analysis
  • Text Mining for TA– Semi-automated taxonomy development
    • Apply data methods, predictive analytics to unstructured text
    • New Models – Watson ensemble methods, reasoning apps
  • Extraction – smarter extraction – sections of documents, Boolean, advanced rules – drug names, adverse events – major mention
text analytics world integration of text and data analytics
Text Analytics WorldIntegration of Text and Data Analytics
  • Expertise Location: Case Study: Data and Text
  • Data Sources:
    • HR Information: Geography, Title-Grade, years of experience, education, projects worked on, hours logged, etc.
  • Text Sources:
    • Document authored (major and minor authors) – data and/or text
    • Documents associated (teams, themes) – categorized to a taxonomy
    • Experience description – extract concepts, entities
  • Self-reported expertise – requires normalization, quality control
  • Complex judgments:
    • Faceted application
    • Ensemble methods – combine evaluations
text analytics world building on the platform expertise analysis
Text Analytics World : Building on the PlatformExpertise Analysis
  • Expertise Characterization for individuals, communities, documents, and sets of documents
  • Experts prefer lower, subordinate levels
    • Novice & General – high and basic level
  • Experts language structure is different
    • Focus on procedures over content
  • Applications:
    • Business & Customer intelligence – add expertise to sentiment
    • Deeper research into communities, customers
    • Expertise location- Generate automatic expertise characterization based on documents
text analytics world new approaches applied watson
Text Analytics WorldNew Approaches – Applied Watson
  • Key concept is that multiple approaches are required – and a way to combine them – confidence score
  • Aim = 85% accuracy of 50% of questions (Ken Jennings – 92% of 62%
  • Used a combination of structure and text search
  • Massive parallelism, many experts, pervasive confidence estimation, integration of shallow and deep knowledge
  • Key step – fast filtering to get to top 100 (System 1)
  • Then – intense analysis to evaluate (System 2) – multiple scoring
text analytics world new approaches applied watson1
Text Analytics WorldNew Approaches – Applied Watson
  • Multiple sources – taxonomies, ontologies, etc.
  • Special modules – temporal and spatial reasoning – anomalies
  • Taxonomic, Geospatial, Temporal, Source Reliability, Gender, Name Consistency, Relational, Passage Support, Theory Consistency, etc.
  • Merge answer scores before ranking
  • 3 Years, 20 researchers of all types
  • Got to 70% of 70% - in two hours
  • More difficult answers / more complete questions
text analytics world new approaches adding structure to content
Text Analytics WorldNew Approaches: Adding Structure to Content
  • Contexts – whole range of types of context
    • Document types-purpose, Textual complexity, formats
  • Categorization by page, sections (text markers) or even sentence or phrase – Key – remember what the last page was
    • [Key– documents are not unstructured – they have a variety of structures]
  • Use generic components – like the level of generality of terms or concepts (general and context specific)
text analytics world new approaches
Text Analytics WorldNew Approaches
  • Idea – build a higher level language – like tutoring systems
    • More complex primitives
  • IDEA – Crowd sourcing – to evolve better structures – how design to avoid design by committee – other side of wisdom of crowds
  • Design TA Game – 1,000’s to play and evolve
  • Partner with MOOC - example – better essay evaluation – avoid gaming the system – lots of multi-syllabic words – nonsense
    • Also to enhance software / modules
new directions in text analytics conclusions
New Directions in Text AnalyticsConclusions
  • Text Analytics is growing – but
  • Big obstacles remain
    • Strategic Vision of text analytics in the enterprise, applications
    • Concrete and quick application to drive acceptance
    • Software still too complex, un-integrated
  • New models are being developed

Cognitive science – System 1 and 2, AI – brains that learn

Watson like integrated approaches

  • Overcome complexity – modules (System 1/ Standard) with new ways of integrating (System 2 / Customized) – smarter and easier
questions

Questions?

Tom Reamytomr@kapsgroup.com

KAPS Group

http://www.kapsgroup.com

Upcoming: Taxonomy Boot Camp – KMWorld -DC, Nov 3-6

Workshop on Text Analytics

Text Analytics World – San Francisco, March 17-19

future directions for text analytics social media beyond simple sentiment
Future Directions for Text AnalyticsSocial Media: Beyond Simple Sentiment
  • Analysis of Conversations- Higher level context
    • Techniques: self-revelation, humor, sharing of secrets, establishment of informal agreements, private language
    • Detect relationships among speakers and changes over time
    • Strength of social ties, informal hierarchies
  • Combination with other techniques
    • Expertise Analysis – plus Influencers
    • Quality of communication (strength of social ties, extent of private language, amount and nature of epistemic emotions – confusion+)
    • Experiments - Pronoun Analysis – personality types
    • Analysis of phrases, multiple contexts – conditionals, oblique
introduction personal
Introduction: Personal
  • Deep Background: History of Ideas – dissertation – Models of Historical Knowledge
  • Artificial Intelligence research at Stanford AI Lab
  • Programming – designed two computer games, educational software
  • Started an Education Software company, CTO
    • Height of California recession
  • Information Architect – Chiron/Novartis, Schwab Intranet
    • Importance of metadata, taxonomy, search – Verity
  • From technology to semantics, usability
  • From library science to cognitive science
  • 2002 – started consulting company