Text and Beyond

Seth Grimes@sethgrimes#TAS12

A slide from the past…

Vox Populi

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

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.

More

Filtering

Velocity & Volume. (Where’s Variety?)

Down with IT!Up with users!

A Big Data analytics architecture (HPCC’s)

http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html

http://hpccsystems.com/

You can’t have it all?!

Where are the flexibility, the (data/content) sophistication, and real-timedness?

Platform plays; advantage APIs

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.

We’re here

Fusions

“By NetBase”?!

No analytics?

Social media magic (?) (2 examples)

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,…

Question Authority

https://secure.wikimedia.org/wikipedia/en/wiki/File:Watson_Jeopardy.jpg

http://img.freebase.com/api/trans/raw/m/02dtnzv

http://www.cambridgesemantics.com/semantic-university/semantic-search-and-the-semantic-web

The Semantic Web?

A knowledge representation built on an assemblage of standards, protocols, and functions.

A Semanticized Web

Google Knowledge Graph

Text+ technology mashups

Text analytics generates semantics to bridge search, BI, and applications, enabling next-generation information systems.

Search BI

Applica-tions

Search based applications (search + text + apps)

Information access (search + text + BI)

Integrated analytics (text + BI)

Text analytics (inner circle)

Semantic search (search + text)

NextGen CRM, EFM, MR, marketing, …

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

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

Personal. Mobile. Intelligent?

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.

A focus on information & applications

Now and near future.• Signal detection.

Sentiment, emotion, identity, intent.• Semanticized applications.

Linkable, mashable, enrichable.• Rich information.

Context sensitive, situational.

Σ = Sense-making...

… but there’s work to do:

Overall experience / satisfaction

Ability to solve business problems

Solution / technology ease of useSolution / technology performance

Availability of professional services / support

0%

50%

100%

Experience/satisfaction sentiment polarity

Positive

Neutral

Negative

Text Analytics From Sources to Signals to Sense

Seth Grimes@sethgrimes

Next year’s talk? --