Text and Beyond

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Opening talk, 2012 Text Analytics Summit, by Seth Grimes

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Text and Beyond

  1. 1. Text and BeyondSeth Grimes@sethgrimes#TAS12
  2. 2. A slide from the past…
  3. 3. Vox Populi
  4. 4. Milestones [and goal(s)?] (circa 2011)Language+ understanding. • Text, speech, and video. • Narrative, discourse, and argument.Information extraction.Knowledge structuring and integration.Inference; synthesis.Language generation.Conversation; interaction; autonomy.≈> Convergence, a.k.a. Singularity
  5. 5. Text stories of the last 12 months…Big Data: the 3 Vs.APIs, platforms, and cloud services.Acquisitions: Information access. • Autonomy  HP. • Endeca  Oracle. • ISYS  Lexmark. • Vivisimo  IBM.Social media magic (?), e.g., • Oracle Social Network (+ Collective Intellect). • SAP Social Media Analytics.Knowledge, enrichment & integration.
  6. 6. Velocity & Volume. (Where’s Variety?) Filtering MoreDown with IT!Up with users!
  7. 7. A Big Data analytics architecture (HPCC’s)http://hpccsystems.com/ http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html
  8. 8. You can’t have it all?! Where are the flexibility, the (data/content) sophistication, and real- timedness?
  9. 9. Platform plays; advantage APIs
  10. 10. Text stories of the last 12 months…Big Data: the 3 Vs.APIs, platforms, and cloud services. We’reAcquisitions: Information access. here • Autonomy  HP. • Endeca  Oracle. • ISYS  Lexmark. • Vivisimo  IBM.Social media magic (?), e.g., • Oracle Social Network (+ Collective Intellect). • SAP Social Media Analytics.Knowledge, enrichment & integration.
  11. 11. Fusions
  12. 12. Social media magic (?) (2 examples) “By NetBase”?! No analytics? 
  13. 13. Knowledge, enrichment & integrationSemantics enables join across types and/or sources and/or structures, using meaningful identifiers, to create an ensemble that is greater than the sum of the parts.Interrelate information to represent knowledge.Enrichment and integration involve: • Mappings and transformations. • Aggregation and collection. • All the typical data concerns: cleansing, profiling, consistency, security,…
  14. 14. Question Authority https://secure.wikimedia.org/wiki pedia/en/wiki/File:Watson_Jeopar dy.jpg
  15. 15. The Semantic Web? A knowledge representation built on an assemblage of standards, protocols, and functions.http://www.cambridgesemantics.com/semantic-university/semantic-search-and-the-semantic-web http://img.freebase.com/api/trans/raw/m/02dtnzv
  16. 16. A Semanticized WebGoogleKnowledgeGraph
  17. 17. Text+ technology mashupsText analytics generates semantics to bridge search, BI, and applications, enabling next-generation information systems. Semantic search Information access (search + text) (search + text + BI)Search based Search BIapplications Integrated analytics(search + text + (text + BI)apps) Applica- Text analytics tions NextGen CRM, EFM, (inner circle) MR, marketing, …
  18. 18. Milestones [and goal(s)?] re-viewed✔ Language+ understanding. ~ Text, speech, and video. ✖ Narrative, discourse, and argument.✔ Information extraction.✔ Knowledge structuring and integration.? Inference; synthesis.~ Language generation.Conversation; interaction; autonomy.≈> Convergence, a.k.a. Singularity
  19. 19. Personal. Mobile. Intelligent?http://timoelliott.com/blog/2010/10/sap-businessobjects-augmented-explorer-now-available-resources-to-test-it.html
  20. 20. Text tech initiatives (2011 2012)Now and near future. • Beyond-polarity sentiment analysis. Emotions, intent signals. etc. • Identity resolution & profile extraction. Online-social-enterprise data integration. • Semantic data integration, Complex Data. • Speech analytics. • Discourse analysis. Because isolated messages are not conversations. • Rich-media content analytics. • Augmented reality; new human-computer interfaces.
  21. 21. A focus on information & applicationsNow and near future. • Signal detection. Sentiment, emotion, identity, intent. • Semanticized applications. Experience/satisfaction sentiment polarity Linkable, mashable, enrichable. Positive • Rich information. Overall experience / Neutral Context sensitive, situational. satisfaction 80% NegativeΣ = Sense-making... 60% 40% Availability of professional Ability to solve business services / support 20% problems… but there’s work to do: 0% Solution / technology Solution / technology ease of performance use
  22. 22. Next year’s talk? -- Text Analytics From Sources to Signals to Sense Seth Grimes @sethgrimes
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