Text Analytics for Search Applications Workshop Tom Reamy Chief Knowledge Architect KAPS Group...
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Transcript of Text Analytics for Search Applications Workshop Tom Reamy Chief Knowledge Architect KAPS Group...
Text Analyticsfor Search Applications
Workshop
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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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
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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.com
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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
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Introduction to Text AnalyticsText 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
Case Study – Categorization & Sentiment
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Case Study – Categorization & Sentiment
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Introduction to Text AnalyticsTaxonomy & 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
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Introduction to Text AnalyticsTA & 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
Introduction to Text AnalyticsTaxonomy 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
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Introduction to Text AnalyticsMetadata - 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)!
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Introduction to Text AnalyticsContent 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
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Introduction to Text AnalyticsBenefits 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
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Text Analytics Platform – BenefitsIDC 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
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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
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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
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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
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Getting Started with Text AnalyticsText 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
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Getting Started with Text AnalyticsText 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
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
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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
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Step 2: TA EvaluationVarieties 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
Step 2: Text Analytics EvaluationDifferent 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
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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
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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
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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
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Questions?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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Resources
Conferences:– Text Analytics World – All aspects of text analytics
• Call for Speakers – Oct 3-4 Boston– Text Analytics Summit – social media focus
LinkedIn Groups:– Text Analytics World– Text Analytics Group– Data and Text Professionals– Sentiment Analysis– Metadata Management– Semantic Technologies
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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