Enabling Exploration through Text Analytics
Daniel Tunkelang Chief Scientist, Endeca

overview <ul><li>information seeking tools </li></ul><ul><li>need
to support exploration </li></ul><ul><li>text analytics can help </li></ul><ul><li>you can do this here and now </li></ul>

real-world information seeking examples <ul><li>looking
for health information </li></ul><ul><li>looking for work-related information </li></ul><ul><li>reminder </li></ul><ul><li>search and text analytics </li></ul><ul><li>are a means, not an end </li></ul>

example 1: looking for health
information <ul><li>six months into my wife’s pregnancy, we </li></ul><ul><li>discovered that she had gestational diabetes </li></ul><ul><li>how to learn more? </li></ul>

google: the default option for
most

in government we trust: fda.gov

maybe the private sector knows
best: webmd powered by

success – and a sticky
site powered by

example 2: looking for work-related
information <ul><li>need to ramp up summer </li></ul><ul><li>interns on text mining </li></ul><ul><li>how to find a good book? </li></ul>

let’s try google again

google: the gateway to wikipedia?

the library of congress (loc.gov)

triangle research libraries: next-gen catalog
powered by

faceted search enables query refinement
powered by

take-away #1 <ul><li>exploratory search support:
</li></ul><ul><li>a must-have for many information needs </li></ul>

text analytics <ul><li>categorization </li></ul><ul><li>named entity
detection </li></ul><ul><li>term extraction </li></ul><ul><li>sentiment analysis </li></ul><ul><li>vague term, lots of see-alsos </li></ul><ul><li>text mining </li></ul><ul><li>information extraction </li></ul><ul><li>content enrichment </li></ul>

newssift: text analytics enabling exploration
powered by categorization named entity detection term extraction sentiment analysis

exploring the news about facebook
powered by

facebook: the good powered by
Social Utility Iphone Application

facebook: the bad powered by
Criminal Behavior Litigation And Settlement

take-away #2 <ul><li>text analytics enable
</li></ul><ul><li>exploratory search </li></ul>

text analytics is here and
now ? ? ?

lots of off-the-shelf options and
more!

caveats <ul><li>rule-based techniques are domain-specific
</li></ul><ul><li>statistical techniques rely on trained models </li></ul><ul><li>plan for errors, inconsistency </li></ul><ul><li>document vs. corpus analysis </li></ul>

problems with entity extraction <ul><li>moderate
precision, but low recall </li></ul><ul><li>not just noisy, but inconsistent </li></ul><ul><li>corpus analysis can help! </li></ul>Arrest (1) Asia (1) ALTOONA, PA (1) Abe Lincoln (1) Bob Dole (1) Boston Tea Party (1) Abraham Lincoln (1) Budweiser (1) Australia (1) Adlai Stephenson (1) Boston Tea Party (1) Austin, Texas (1) Abraham Lincoln (1) Boston Globe (1) Austin (1) Abe Weiss (1) Bocuse d’Or World Cuisine Contest (1) Atlanta (2) Abe Lincoln (1) Bob Dole (1) Asia (1) Abbie Hoffman (1) Bloomberg LP (3) Arrest (1) Aaron Sorkin (1) BioDiversity Research Institute (1) Arlington, Va. (2) ARYE BARAK (1) Big Apple Companies (1) Arkansas (7) ANTONIN SCALIA (1) Bear Stearns (2) Arizona (11) ANTHONY MWANGI (1) Bad News Bears (1) Argentina (1) ANDREW LLOYD WEBBER (1) Australian Liberal Party (1) Appalachia (1) ANDERS ERICSSON (1) Arianna Huffington (1) Americas (17) AMY WINEHOUSE (1) Arctic National Wildlife Refuge (1) Allegheny (1) AMANDA MARCOTTE (1) Apple (1) Alaska (3) ALI HASSAN AL (1) American Airlines Inc. (1) Akihabara (1) ALEX TREBEK (1) Amazon.com Inc. (1) Africa (5) AL GORE (1) Air Force (1) Afghanistan (7) ABDULRAHMAN ABDULLAH (1) ABC News Inc. (1) ALTOONA, PA (1) ABDUL-KARIM KHALAF (1) Organization Location Person

look for ways to cheat!
recall precision

division of labor people supply
vocabulary machine annotates documents http://www.precolumbianwomen.com/images/inca-labor.10.gif

example: ACM digital library <ul><li>opportunity
</li></ul><ul><ul><li>repository of (sometimes) author-tagged documents </li></ul></ul><ul><ul><li>high-precision tags: very few false positives </li></ul></ul><ul><li>challenge </li></ul><ul><ul><li>poor reuse of vocabulary: most tags unique </li></ul></ul><ul><ul><li>low-recall tags: 90% false negatives </li></ul></ul><ul><li>as is, tags were not useful for exploration </li></ul>

solution <ul><li>bootstrap on author-supplied tags
</li></ul><ul><li>prune 600K+ tags to 10K by </li></ul><ul><ul><li>imposing frequency threshold </li></ul></ul><ul><ul><li>normalizing by case and singular/plural </li></ul></ul><ul><ul><li>eliminating infrequent subphrases </li></ul></ul><ul><li>mine documents using resulting vocabulary </li></ul><ul><li>manually validate most frequently assigned tags </li></ul>

example: a search for boeing
powered by

it’s a HITS!

if you prefer sports to
computer science <ul><li>no author-supplied tags </li></ul><ul><li>use search logs instead </li></ul><ul><li>supplement with authority files </li></ul><ul><ul><li>team names </li></ul></ul><ul><ul><li>player names </li></ul></ul><ul><li>mine documents using resulting vocabulary </li></ul>

roger clemens, then and now
powered by

pivoting to a different view
powered by

take-away #3 <ul><li>this is not
vapor ware; </li></ul><ul><li>text analytics to enable exploration </li></ul><ul><li>is available here and now </li></ul>

looking forward <ul><li>better tags are
the beginning, not the end </li></ul><ul><li>improve with manual and automatic processing </li></ul><ul><li>give users control over precision / recall trade-off </li></ul><ul><li>help users and content creators help you </li></ul>

in closing <ul><li>exploratory search =
must-have, not nice-to-have </li></ul><ul><li>text analytics are a key enabler </li></ul><ul><li>the technology is real, here, and now </li></ul>

thank you…and come to SIGIR!
<ul><li>communication 1.0 </li></ul><ul><li>email: [email_address] </li></ul><ul><li>communication 2.0 </li></ul><ul><li>blog: http://thenoisychannel.com </li></ul><ul><li>twitter: http://twitter.com/dtunkelang </li></ul><ul><li>SIGIR: July 19-23 in Boston </li></ul><ul><li>Industry Track on July 22 nd ! </li></ul>