Enabling Exploration Through Text Analytics

© 2009 Endeca Technologies, Inc. All rights reserved.

Enabling Exploration through Text Analytics

Daniel TunkelangChief Scientist, Endeca

© 2009 Endeca Technologies, Inc. All rights reserved.2

overview

information seeking toolsneed to support exploration

text analytics can help

you can do this here and now

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real-world information seeking examples

• looking for health information

• looking for work-related information

remindersearch and text analyticsare a means, not an end

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example 1: looking for health information

six months into my wife’s pregnancy, wediscovered that she had gestational diabetes

how to learn more?

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google: the default option for most

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in government we trust: fda.gov

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maybe the private sector knows best: webmd

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success – and a sticky site

powered by

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example 2: looking for work-related information

need to ramp up summerinterns on text mining

how to find a good book?

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let’s try google again

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google: the gateway to wikipedia?

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the library of congress (loc.gov)

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triangle research libraries: next-gen catalog

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faceted search enables query refinement

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take-away #1

exploratory search support:a must-have for many information needs

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text analytics

• categorization• named entity detection• term extraction• sentiment analysis

vague term, lots of see-alsostext mining

information extractioncontent enrichment

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newssift: text analytics enabling exploration

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categorization

named entity detection

term extraction

sentiment analysis

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exploring the news about facebook

powered by

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facebook: the good

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Social Utility

Iphone Application

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facebook: the bad

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Criminal BehaviorLitigation AndSettlement

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take-away #2

text analytics enableexploratory search

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text analytics is here and now

? ??

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lots of off-the-shelf options

and more!

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caveats

• rule-based techniques are domain-specific

• statistical techniques rely on trained models

• plan for errors, inconsistency

• document vs. corpus analysis

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Person Location Organization

ABDUL-KARIM KHALAF (1) ALTOONA, PA (1) ABC News Inc. (1)

ABDULRAHMAN ABDULLAH (1) Afghanistan (7) Air Force (1)

AL GORE (1) Africa (5) Amazon.com Inc. (1)

ALEX TREBEK (1) Akihabara (1) American Airlines Inc. (1)

ALI HASSAN AL (1) Alaska (3) Apple (1)

AMANDA MARCOTTE (1) Allegheny (1) Arctic National Wildlife Refuge (1)

AMY WINEHOUSE (1) Americas (17) Arianna Huffington (1)

ANDERS ERICSSON (1) Appalachia (1) Australian Liberal Party (1)

ANDREW LLOYD WEBBER (1) Argentina (1) Bad News Bears (1)

ANTHONY MWANGI (1) Arizona (11) Bear Stearns (2)

ANTONIN SCALIA (1) Arkansas (7) Big Apple Companies (1)

ARYE BARAK (1) Arlington, Va. (2) BioDiversity Research Institute (1)

Aaron Sorkin (1) Arrest (1) Bloomberg LP (3)

Abbie Hoffman (1) Asia (1) Bob Dole (1)

Abe Lincoln (1) Atlanta (2) Bocuse d’Or World Cuisine Contest (1)

Abe Weiss (1) Austin (1) Boston Globe (1)

Abraham Lincoln (1) Austin, Texas (1) Boston Tea Party (1)

Adlai Stephenson (1) Australia (1) Budweiser (1)

problems with entity extraction

• moderate precision, but low recall• not just noisy, but inconsistent• corpus analysis can help!

Arrest (1)

Asia (1)

ALTOONA, PA (1)

Abe Lincoln (1)

Bob Dole (1)

Boston Tea Party (1)Abraham Lincoln (1)

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look for ways to cheat!

recall

precision

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division of labor

people supply vocabulary

machine annotates documents

http://www.precolumbianwomen.com/images/inca-labor.10.gif

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example: ACM digital library

• opportunity– repository of (sometimes) author-tagged documents– high-precision tags: very few false positives

• challenge– poor reuse of vocabulary: most tags unique– low-recall tags: 90% false negatives

as is, tags were not useful for exploration

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solution

• bootstrap on author-supplied tags

• prune 600K+ tags to 10K by– imposing frequency threshold– normalizing by case and singular/plural– eliminating infrequent subphrases

• mine documents using resulting vocabulary

• manually validate most frequently assigned tags

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example: a search for boeing

powered by

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it’s a HITS!

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if you prefer sports to computer science

• no author-supplied tags

• use search logs instead

• supplement with authority files– team names– player names

• mine documents using resulting vocabulary

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roger clemens, then and now

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pivoting to a different view

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take-away #3

this is not vapor ware;text analytics to enable exploration

is available here and now

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looking forward

• better tags are the beginning, not the end

• improve with manual and automatic processing

• give users control over precision / recall trade-off

• help users and content creators help you

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in closing

exploratory search = must-have, not nice-to-have

text analytics are a key enabler

the technology is real, here, and now

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thank you…and come to SIGIR!

communication 1.0email: dt@endeca.com

communication 2.0blog: http://thenoisychannel.com

twitter: http://twitter.com/dtunkelang

SIGIR: July 19-23 in Boston Industry Track on July 22nd!