Opening talk, 2012 Text Analytics Summit, by Seth Grimes

1. Text and Beyond
Seth Grimes
@sethgrimes
#TAS12

2. A slide from the past…

3. Vox Populi

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. 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. Velocity & Volume. (Where’s Variety?)
Filtering
More
Down with IT!
Up with users!

7. A Big Data analytics architecture
(HPCC’s)
http://hpccsystems.com/
http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html

8. You can’t have it all?!
Where are the
flexibility, the
(data/content)
sophistication,
and real-
timedness?

9. Platform plays; advantage APIs

10. Text stories of the last 12 months…
Big Data: the 3 Vs.
APIs, platforms, and cloud services.
We’re
Acquisitions: 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. Fusions

12. Social media magic (?) (2 examples)
 “By NetBase”?!
No analytics? 

13. Knowledge, enrichment & integration
Semantics 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. Question Authority
https://secure.wikimedia.org/wiki
pedia/en/wiki/File:Watson_Jeopar
dy.jpg

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. A Semanticized Web
Google
Knowledge
Graph

17. Text+ technology mashups
Text 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 BI
applications
Integrated analytics
(search + text +
(text + BI)
apps)
Applica-
Text analytics tions NextGen CRM, EFM,
(inner circle) MR, marketing, …

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. Personal. Mobile. Intelligent?
http://timoelliott.com/blog/2010/10/sap-businessobjects-augmented-
explorer-now-available-resources-to-test-it.html

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. A focus on information & applications
Now 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% Negative
Σ = 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. Next year’s talk? --
Text Analytics 
From Sources to Signals to Sense
Seth Grimes
@sethgrimes