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Enterprises are awash in textual documents that represent valuable information assets. The limited access of conventional search interfaces, however, prevents enterprises from unlocking this value; * An expert guide to how richer interfaces enable exploration and discovery and how these typically rely on content enrichment techniques that can be unreliable, labor-intensive, or both. It is essential to maximize the effectiveness of content enrichment, not only to achieve the desired value, but also to incent organizations to make the necessary investment. * Useful insight about content enrichment approaches that have demonstrated success in supporting exploration and discovery. * Gain insight into both the enrichment techniques and the ways they are used to enable exploratory search.Daniel Tunkelang, Chief Scientist, Endeca
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© 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
© 2009 Endeca Technologies, Inc. All rights reserved.3
real-world information seeking examples
• looking for health information
• looking for work-related information
remindersearch and text analyticsare a means, not an end
© 2009 Endeca Technologies, Inc. All rights reserved.4
example 1: looking for health information
six months into my wife’s pregnancy, wediscovered that she had gestational diabetes
how to learn more?
© 2009 Endeca Technologies, Inc. All rights reserved.5
google: the default option for most
© 2009 Endeca Technologies, Inc. All rights reserved.6
in government we trust: fda.gov
© 2009 Endeca Technologies, Inc. All rights reserved.7
maybe the private sector knows best: webmd
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.8
success – and a sticky site
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.9
example 2: looking for work-related information
need to ramp up summerinterns on text mining
how to find a good book?
© 2009 Endeca Technologies, Inc. All rights reserved.10
let’s try google again
© 2009 Endeca Technologies, Inc. All rights reserved.11
google: the gateway to wikipedia?
© 2009 Endeca Technologies, Inc. All rights reserved.12
the library of congress (loc.gov)
© 2009 Endeca Technologies, Inc. All rights reserved.13
triangle research libraries: next-gen catalog
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.14
faceted search enables query refinement
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.15
take-away #1
exploratory search support:a must-have for many information needs
© 2009 Endeca Technologies, Inc. All rights reserved.16
text analytics
• categorization• named entity detection• term extraction• sentiment analysis
vague term, lots of see-alsostext mining
information extractioncontent enrichment
© 2009 Endeca Technologies, Inc. All rights reserved.17
newssift: text analytics enabling exploration
powered by
categorization
named entity detection
term extraction
sentiment analysis
© 2009 Endeca Technologies, Inc. All rights reserved.18
exploring the news about facebook
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.19
facebook: the good
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Social Utility
Iphone Application
© 2009 Endeca Technologies, Inc. All rights reserved.20
facebook: the bad
powered by
Criminal BehaviorLitigation AndSettlement
© 2009 Endeca Technologies, Inc. All rights reserved.21
take-away #2
text analytics enableexploratory search
© 2009 Endeca Technologies, Inc. All rights reserved.22
text analytics is here and now
? ??
© 2009 Endeca Technologies, Inc. All rights reserved.23
lots of off-the-shelf options
and more!
© 2009 Endeca Technologies, Inc. All rights reserved.24
caveats
• rule-based techniques are domain-specific
• statistical techniques rely on trained models
• plan for errors, inconsistency
• document vs. corpus analysis
© 2009 Endeca Technologies, Inc. All rights reserved.25
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)
© 2009 Endeca Technologies, Inc. All rights reserved.26
look for ways to cheat!
recall
precision
© 2009 Endeca Technologies, Inc. All rights reserved.27
division of labor
people supply vocabulary
machine annotates documents
http://www.precolumbianwomen.com/images/inca-labor.10.gif
© 2009 Endeca Technologies, Inc. All rights reserved.28
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
© 2009 Endeca Technologies, Inc. All rights reserved.29
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
© 2009 Endeca Technologies, Inc. All rights reserved.30
example: a search for boeing
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.31
it’s a HITS!
© 2009 Endeca Technologies, Inc. All rights reserved.32
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
© 2009 Endeca Technologies, Inc. All rights reserved.33
roger clemens, then and now
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.34
pivoting to a different view
powered by
© 2009 Endeca Technologies, Inc. All rights reserved.35
take-away #3
this is not vapor ware;text analytics to enable exploration
is available here and now
© 2009 Endeca Technologies, Inc. All rights reserved.36
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
© 2009 Endeca Technologies, Inc. All rights reserved.37
in closing
exploratory search = must-have, not nice-to-have
text analytics are a key enabler
the technology is real, here, and now
© 2009 Endeca Technologies, Inc. All rights reserved.38
thank you…and come to SIGIR!
communication 1.0email: [email protected]
communication 2.0blog: http://thenoisychannel.com
twitter: http://twitter.com/dtunkelang
SIGIR: July 19-23 in Boston Industry Track on July 22nd!