Text Analytics Software
Choosing the Right Fit
Tom Reamy
Chief Knowledge Architect
KAPS Group
http://www.kapsgroup.com
Text Analytics World
October 20 New York
Agenda
 Introduction – Text Analytics Basics
 Evaluation Process & Methodology
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Two Stages – Initial Filters & POC
 Proof of Concept
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Methodology
– Results
 Text Analytics and “Text Analytics”
 Conclusions
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KAPS Group: General
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Knowledge Architecture Professional Services
Virtual Company: Network of consultants – 8-10
Partners – SAS, SAP, FAST, Smart Logic, Concept Searching, etc.
Consulting, Strategy, Knowledge architecture audit
Services:
– Taxonomy/Text Analytics development, consulting, customization
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Evaluation of Enterprise Search, Text Analytics
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Text Analytics Assessment, Fast Start
– Technology Consulting – Search, CMS, Portals, etc.
– Knowledge Management: Collaboration, Expertise, e-learning
– Applied Theory – Faceted taxonomies, complexity theory, natural
categories
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Introduction to Text Analytics
Text Analytics Features
 Noun Phrase Extraction
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Catalogs with variants, rule based dynamic
– Multiple types, custom classes – entities, concepts, events
– Feeds facets
 Summarization
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Customizable rules, map to different content
 Fact Extraction
Relationships of entities – people-organizations-activities
– Ontologies – triples, RDF, etc.
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 Sentiment Analysis
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Rules – Objects and phrases
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Introduction to Text Analytics
Text Analytics Features
 Auto-categorization
Training sets – Bayesian, Vector space
– Terms – literal strings, stemming, dictionary of related terms
– Rules – simple – position in text (Title, body, url)
– Semantic Network – Predefined relationships, sets of rules
– Boolean– Full search syntax – AND, OR, NOT
– Advanced – DIST(#), ORDDIST#, PARAGRAPH, SENTENCE
This is the most difficult to develop
Build on a Taxonomy
Combine with Extraction
– If any of list of entities and other words
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Case Study – Categorization & Sentiment
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Case Study – Categorization & Sentiment
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Evaluation Process & Methodology
Overview
 Start with Self Knowledge
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Think Big, Start Small, Scale Fast
 Eliminate the unfit
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Filter One- Ask Experts - reputation, research – Gartner, etc.
• Market strength of vendor, platforms, etc.
• Feature scorecard – minimum, must have, filter to top 3
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Filter Two – Technology Filter – match to your overall scope
and capabilities – Filter not a focus
Filter Three – In-Depth Demo – 3-6 vendors
 Deep POC (2) – advanced, integration, semantics
 Focus on working relationship with vendor.
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Design of the Text Analytics Selection Team
Traditional Candidates – IT&, Business, Library
 IT - Experience with software purchases, needs assess, budget
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Search/Categorization is unlike other software, deeper look
 Business -understand business, focus on business value
 They can get executive sponsorship, support, and budget
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But don’t understand information behavior, semantic focus
 Library, KM - Understand information structure
 Experts in search experience and categorization
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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
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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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Evaluating Taxonomy/Text Analytics Software
Start with Self Knowledge
 Strategic and Business Context
 Info Problems – what, how severe
 Strategic Questions – why, what value from the text analytics,
how are you going to use it
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Platform or Applications?
 Formal Process - KA audit – content, users, technology, business
and information behaviors, applications - Or informal for smaller
organization,
 Text Analytics Strategy/Model – forms, technology, people
– Existing taxonomic resources, software
 Need this foundation to evaluate and to develop
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Varieties of Taxonomy/ Text Analytics Software
 Taxonomy Management
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Synaptica, SchemaLogic
 Full Platform
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SAS, SAP, Smart Logic, Linguamatics, Concept Searching, Expert
System, IBM, GATE
 Embedded – Search or Content Management
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FAST, Autonomy, Endeca, Exalead, etc.
– Nstein, Interwoven, Documentum, etc.
 Specialty / Ontology (other semantic)
Sentiment Analysis – Lexalytics, Clarabridge, Lots of players
– Ontology – extraction, plus ontology
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Vendors of Taxonomy/ Text Analytics Software
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Attensity
Business Objects –
Inxight
Clarabridge
ClearForest
Concept Searching
Data Harmony / Access
Innovations
Expert Systems
GATE (Open Source)
IBM Infosphere
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Lexalytics
Multi-Tes
Nstein
SAS
SchemaLogic
Smart Logic
Synaptica
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Initial Evaluation – Factors
Traditional Software Evaluation - Deeper
 Basic & Advanced Capabilities
 Lack of Essential Feature
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No Sentiment Analysis, Limited language support
 Customization vs. OOB
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Strongest OOB – highest customization cost
 Company experience, multiple products vs. platform
 Ease of integration – API’s, Java
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Internal and External Applications
– Technical Issues, Development Environment
 Total Cost of Ownership and support, initial price
 POC Candidates – 1-4
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Initial Evaluation – Factors
Case Studies
 Amdocs
Customer Support Notes – short, badly written, millions of documents
– Total Cost, multiple languages, Integration with their application
– Distributed expertise
– Platform – resell full range of services, Sentiment Analysis
– Twenty to Four to POC (Two) to SAS
GAO
– Library of 200 page PDF formal documents, plus public web site
– People – library staff – 3-4 taxonomists – centralized expertise
– Enterprise search, general public
– Twenty to POC with SAS
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Phase II - Proof Of Concept - POC
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Measurable Quality of results is the essential factor
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
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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POC Design: Evaluation Criteria & Issues
 Basic Test Design – categorize test set
– Score – by file name, human testers
 Categorization & Sentiment – Accuracy 80-90%
– Effort Level per accuracy level
 Quantify development time – main elements
 Comparison of two vendors – how score?
– Combination of scores and report
 Quality of content & initial human categorization
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Normalize among different test evaluators
 Quality of taxonomists – experience with text analytics software and/or
experience with content and information needs and behaviors
 Quality of taxonomy – structure, overlapping categories
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Text Analytics POC Outcomes
Evaluation Factors
 Variety & Limits of Content
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Twitter to large formal libraries
 Quality of Categorization
Scores – Recall, Precision (harder)
– Operators – NOT, DIST, START,
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 Development Environment & Methodology
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Toolkit or Integrated Product
– Effort Level and Usability
 Importance of relevancy – can be used for precision, applications
 Combination of workbench, statistical modeling
 Measures – scores, reports, discussions
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POC and Early Development: Risks and Issues
 CTO Problem –This is not a regular software process
 Semantics is messy not just complex
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30% accuracy isn’t 30% done – could be 90%
 Variability of human categorization
 Categorization is iterative, not “the program works”
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Need realistic budget and flexible project plan
 Anyone can do categorization
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Librarians often overdo, SME’s often get lost (keywords)
 Meta-language issues – understanding the results
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Need to educate IT and business in their language
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Text Analytics and “Text Analytics” – Text Mining
 TA is pre-processing for text mining
 TA adds huge dimensions of unstructured text
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Now 85-90% of all content, Social Media
 TA can improve the quality of text
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Categorization, Disambiguated metadata extraction
 Unstructured text into data - What are the possibilities?
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New Kinds of Taxonomies – emotion, small smart modular
Information Overload – search, facets, auto-tagging, etc.
Behavior Prediction – individual actions (cancel or not?)
Customer & Business Intelligence – new relationships
Crowd sourcing – technical support
Expertise Analysis – documents, authors, communities
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Conclusion
 Start with self-knowledge – what will you use it for?
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Current Environment – technology, information
 Basic Features are only filters, not scores
 Integration – need an integrated team (IT, Business, KA)
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For evaluation and development
 POC – your content, real world scenarios – not scores
 Foundation for development, experience with software
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Development is better, faster, cheaper
 Categorization is essential, time consuming
 Text Analytics opens up new worlds of applications
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Questions?
Tom Reamy
[email protected]
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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