1. Text Analytics for Search Applications Workshop Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. 2 Agenda  Introduction – Text Analytics & Infrastructure Platform – Text Analytics Features – Semantic Infrastructure – Taxonomy, Metadata, Technology – Value of Text Analytics – Getting Started with Text Analytics  Development – Taxonomy, Categorization, Faceted Metadata  Text Analytics Applications – Integration with Search and ECM – Platform for Information Applications  Questions / Discussions

3. 3 KAPS Group: General  Knowledge Architecture Professional Services – Network of Consultants  Partners – SAS, SAP, IBM, FAST, Smart Logic, Concept Searching – Attensity, Clarabridge, Lexalytics,  Strategy – IM & KM - Text Analytics, Social Media, Integration  Services: – Taxonomy/Text Analytics development, consulting, customization – Text Analytics Quick 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, etc.  Applied Theory – Faceted taxonomies, complexity theory, natural categories, emotion taxonomies Presentations, Articles, White Papers – http://www.kapsgroup.comhttp://www.kapsgroup.com

4. 4 Agenda – Introduction Text Analytics & Semantic Infrastructure  Text Analytics Features – Categorization & Extraction  Semantic Infrastructure – Taxonomy, Metadata, Technology  Value of Text Analytics – Enterprise Search that works  Getting Started with Text Analytics – Text Analytics Strategy & Vision – Text Analytics Evaluation / Quick Start

5. 5 Introduction to Text Analytics Text Analytics Features  Noun Phrase Extraction / Fact Extraction – Catalogs with variants, rule based dynamic – Relationships of entities – people-organizations-activities  Sentiment Analysis – Objects and phrases – statistics & rules – Positive and Negative  Summarization – replace snippets  Auto-categorization – built on a taxonomy – Training sets, Terms, Semantic Networks – Rules: AND, OR, NOT, DIST, PARAGRAPH, SENTENCE  Auto-categorization as Foundation – Disambiguation - Identification of objects, events, context – Build rules based, not simply Bag of Individual Words

6. Case Study – Categorization & Sentiment 6

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9. 9 Introduction to Text Analytics Taxonomy & Metadata  Thesauri, Controlled Vocabulary, Glossaries, Product Catalogs – Resources to build on  SharePoint – Managed Metadata Services – Term stores – corporate taxonomies – Enterprise Keywords (Folksonomy)  Metadata standards – Dublin Core - Mostly syntactic not semantic – Semantic – keywords – very poor performance, no structure  Facets – classes of metadata – Standard - People, Organization, Document type-purpose – Requires huge amounts of metadata

10. 10 Introduction to Text Analytics TA & Taxonomy Complimentary Information Platform  Taxonomy provides a consistent and common vocabulary – Enterprise resource – integrated not centralized  Text Analytics provides a consistent tagging – Human indexing is subject to inter and intra individual variation  Taxonomy provides the basic structure for categorization – And candidates terms  Text Analytics provides the power to apply the taxonomy – And metadata of all kinds  Text Analytics and Taxonomy Together – Platform – Consistent in every dimension – Powerful and economic

11. Introduction to Text Analytics Taxonomy and Text Analytics  Standard Taxonomies = starter categorization rules – Example – Mesh – bottom 5 layers are terms  Categorization taxonomy structure – Tradeoff of depth and complexity of rules – Easier to maintain taxonomy, but need to refine rules  Analysis of taxonomy – suitable for categorization – Structure – not too flat, not too large – Orthogonal categories  Smaller modular taxonomies – More flexible relationships – not just Is-A-Kind/Child-Of  Different kinds of taxonomies – Sentiment – products and features Taxonomy of Sentiment, Emotion - Expertise – process 11

12. 12 Introduction to Text Analytics Metadata - Tagging  How do you bridge the gap – taxonomy to documents?  Tagging documents with taxonomy nodes is tough – And expensive – central or distributed  Library staff –experts in categorization not subject matter – Too limited, narrow bottleneck – Often don’t understand business processes and business uses  Authors – Experts in the subject matter, terrible at categorization – Intra and Inter inconsistency, “intertwingleness” – Choosing tags from taxonomy – complex task – Folksonomy – almost as complex, wildly inconsistent – Resistance – not their job, cognitively difficult = non-compliance  Text Analytics is the answer(s)!

13. 13 Introduction to Text Analytics Content Management – SharePoint  Mind the Gap – Manual, Automatic, Hybrid  All require human effort – issue of where and how effective  Manual - human effort is tagging (difficult, inconsistent)  Automatic and Hybrid - human effort is prior to tagging – Build on expertise – librarians on categorization, SME’s on subject terms  Hybrid Model – Publish Document -> Text Analytics analysis -> suggestions for categorization, entities, metadata - > present to author – Cognitive task is simple -> react to a suggestion instead of select from head or a complex taxonomy – Feedback – if author overrides -> suggestion for new category – Facets – Requires a lot of Metadata - Entity Extraction feeds facets  Hybrid – Automatic is really a spectrum – depends on context

14. 14 Introduction to Text Analytics Benefits of Text Analytics  Why Text Analytics? – Enterprise search has failed to live up to its potential – Enterprise Content management has failed to live up to its potential – Taxonomy has failed to live up to its potential – Adding metadata, especially keywords has not worked  What is missing? – Intelligence – human level categorization, conceptualization – Infrastructure – Integrated solutions not technology, software  Text Analytics can be the foundation that (finally) drives success – search, content management, and much more

15. 15 Text Analytics Platform – Benefits IDC White Paper  Time Wasted – Reformat information - $5.7 million per 1,000 per year – Not finding information - $5.3 million per 1,000 – Recreating content - $4.5 Million per 1,000  Small Percent Gain = large savings – 1% - $10 million – 5% - $50 million – 10% - $100 million

16. 16 Text Analytics Platform – Benefits  Findability within and outside the enterprise – Savings per year - $millions  Rescue enterprise search and ECM projects – Add semantics to search  Clean up enterprise content – Duplication and accurate categorization  Improve the quality of information access – Finding the right information can save millions  Build smarter applications – Social networking, locate expertise within the enterprise

17. 17 Text Analytics Platform – Benefits  Understand your customers – What they are talking about and how they feel about it  Empower your employees – Not only more time, but they work smarter  Understand your competitors – What they are working on, talking about – Combine unstructured content and rich data sources – more intelligent analysis

18. 18 Text Analytics Platform – Dangers  Text Analytics as a software project  Not enough resources – to develop, to maintain-refine  Wrong resources – SME’s, IT, Library – Need all of the above and taxonomists+  Bad Design: – Start with bad taxonomy – Wrong taxonomy – too big or two flat  Bad Categorization / Entity Extraction – Right kind of experience

19. 19 Getting Started with Text Analytics Text Analytics Vision & Strategy  Strategic Questions – why, what value from the text analytics, how are you going to use it – Platform or Applications?  What are the basic capabilities of Text Analytics?  What can Text Analytics do for Search? – After 10 years of failure – get search to work?  What can you do with smart search based applications? – RM, PII, Social  ROI for effective search – difficulty of believing – Problems with metadata, taxonomy

20. 20 Getting Started with Text Analytics Text Analytics Vision & Strategy  Simple Subject Taxonomy structure – Easy to develop and maintain  Combined with categorization capabilities – Added power and intelligence  Combined with people tagging, refining tags  Combined with Faceted Metadata – Dynamic selection of simple categories – Allow multiple user perspectives Can’t predict all the ways people think Monkey, Banana, Panda  Combined with ontologies and semantic data – Multiple applications – Text mining to Search – Combine search and browse

21. Step 1 : TA Information Audit Start with Self Knowledge  Info Problems – what, how severe  Formal Process - KA audit – content, users, technology, business and information behaviors, applications - Or informal for smaller organization,  Contextual interviews, content analysis, surveys, focus groups, ethnographic studies, Text Mining  Category modeling – Cognitive Science – how people think  Natural level categories mapped to communities, activities Novice prefer higher levels Balance of informative and distinctiveness  Text Analytics Strategy/Model – forms, technology, people 21

22. Step 1 : TA Information Audit Start with Self Knowledge  Ideas – Content and Content Structure – Map of Content – Tribal language silos – Structure – articulate and integrate – Taxonomic resources  People – Producers & Consumers – Communities, Users, Central Team  Activities – Business processes and procedures – Semantics, information needs and behaviors – Information Governance Policy  Technology – CMS, Search, portals, text analytics – Applications – BI, CI, Semantic Web, Text Mining 22

23. 23 Step 2: TA Evaluation Varieties of Taxonomy/ Text Analytics Software  Taxonomy Management - extraction  Full Platform – SAS, SAP, Smart Logic, Concept Searching, Expert System, IBM, Linguamatics, GATE  Embedded – Search or Content Management – FAST, Autonomy, Endeca, Vivisimo, NLP, etc. – Interwoven, Documentum, etc.  Specialty / Ontology (other semantic) – Sentiment Analysis – Attensity, Lexalytics, Clarabridge, Lots – Ontology – extraction, plus ontology

24. Step 2: Text Analytics Evaluation Different Kind of software evaluation  Traditional Software Evaluation - Start – Filter One- Ask Experts - reputation, research – Gartner, etc. Market strength of vendor, platforms, etc. Feature scorecard – minimum, must have, filter to top 6 – Filter Two – Technology Filter – match to your overall scope and capabilities – Filter not a focus – Filter Three – In-Depth Demo – 3-6 vendors  Reduce to 1-3 vendors  Vendors have different strengths in multiple environments – Millions of short, badly typed documents, Build application – Library 200 page PDF, enterprise & public search 24

25. Design of the Text Analytics Selection Team Traditional Candidates – IT&, Business, Library  IT - Experience with software purchases, needs assess, budget – Search/Categorization is unlike other software, deeper look  Business -understand business, focus on business value  They can get executive sponsorship, support, and budget – But don’t understand information behavior, semantic focus  Library, KM - Understand information structure  Experts in search experience and categorization – But don’t understand business or technology 25

26. Design of the Text Analytics Selection Team  Interdisciplinary Team, headed by Information Professionals  Relative Contributions – IT – Set necessary conditions, support tests – Business – provide input into requirements, support project – Library – provide input into requirements, add understanding of search semantics and functionality  Much more likely to make a good decision  Create the foundation for implementation 26

27. Step 3: Proof of Concept / Pilot Project  4 weeks POC – bake off / or short pilot  Real life scenarios, categorization with your content  2 rounds of development, test, refine / Not OOB  Need SME’s as test evaluators – also to do an initial categorization of content  Measurable Quality of results is the essential factor  Majority of time is on auto-categorization  Need to balance uniformity of results with vendor unique capabilities – have to determine at POC time  Taxonomy Developers – expert consultants plus internal taxonomists 27

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

29. 29 Resources  Conferences: – Text Analytics World – All aspects of text analytics Text Analytics World Call for Speakers – Oct 3-4 Boston – Text Analytics Summit – social media focus Text Analytics Summit  LinkedIn Groups: – Text Analytics World – Text Analytics Group – Data and Text Professionals – Sentiment Analysis – Metadata Management – Semantic Technologies

30. 30 Resources  Books – Women, Fire, and Dangerous Things George Lakoff – Knowledge, Concepts, and Categories Koen Lamberts and David Shanks – The Stuff of Thought – Steven Pinker  Journals – Academic – Cognitive Science, Linguistics, NLP – Applied – Scientific American Mind, New Scientist