Web Scale Named Entity Mining

Web scale Named Entity Mining"Theres simply too much information out there" WI-IAT 2011

in memoriam ofHerbert A. Simon …

stuckApril 2011

Herbert Simons Brookings Institute Lecture"Designing Organizations for an Information-Rich World" Johns Hopkins University, September 1, 1969

1.Tales & legends

Find & procure a crystal plastic replacement of a polycarbonate LEXAN 943Main constraints: •more resistant to detergent agents than LEXAN 943 (problem of cracking under combined effect of mechanical stress and exposure to detergent agents) •compatible with existing tools - withdrawal must be close to LEXAN 943 •optical characteristic close to LEXAN 943 •weldable by ultrasonic welding •compliant with resistance to fire & smoke requirements 2 according to NFF16-101/102 and V0 according standard UL 94 delay : one week organization centric search

Where is sold/operated the SA-24 Grinch 9K338 Igla-S portable air defense missile system ? location centric search

Recent information (past month) about call for proposal"outils Web innovants en entreprise" ? time centric search

"pro" searches focus on Orgs PeopleLocation Time named entities

2.Introducing WebNEM

product find getspecifications compliant manufacturer products or distributor where ? relevant + browsing/ranking query query ? results again ? Attention-greedy & burdensome

Goal : Attention-saver process"SA-24 Grinch9K338 Igla-S"

exploratory data analysisof high dimensional data

Lourenço, Lobo, Bação – JOCLAD 2004"In exploratory data analysis of high dimensional data one of the main tasks is the formation of a simplified, usually visual, overview of data sets. .... Clustering and projection are among the examples of useful methods to achieve this task." Fernando Lourenco, Victor Lobo, Fernando Bacao: Binary-based similarity measures for categorical data and theirapplication in self-organizing maps. JOCLAD 2004 - XI Jornadas de Classificacao e Anlise de Dados, April 1-3 , Lisbon (2004)

WebNEM collection of relevant data, anywhere in the web + projection on Named Entities space topical web crawler named entity recognitionvisualization/exploratory analysis tools

"Web scale" collection : brute force a priori "everywhere""whole" Web indexing never-ending crawl huge resources required (data size based) general index user fast answer, query "any" topic

"Web scale" collection : our approach user anywhere query relevant "close to optimal" resources on-demand topical crawl (usage based) tailored index built on order delayed answer, Web slices but less garbage

Projection : when to extract entities ? Named Entity Recognition is resource intensive process step data size required response timequery time collection 102 real-timecrawl time whole web 1010 asynchronouscrawl time web slice 104 asynchronous

our SaaS Web mining system large scaleNamed Entity extraction (EN/FR) beta released to customers June 2011 www.squido.fr

WebNEM with Squidosearch focused page shallow topic crawl cleaning entity extraction userqueries index visualization user deep visualizationcollections entity extraction

Page cleaning instead of this work on this fast heuristic DOM processing

Shallow extractionWeb detect sentence parse tokenizedocs language splitgazetteers grammar format index

Deep extraction ≅ shallow extraction + elaborate linguistics POS grammar tagger ortho morpho NP/VPmatcher analyzer chunker index

3.Annoyances

Linguistic processing throughput deep extraction shallow too expensive extraction when crawling OKworkaround : penalty asynch deep extraction onon smaller collections query time sanitization quality

Page cleaninggoal : ↗accuracy ? cost : ↘ recall ? performance impact ? ↘ +1 processing step ↗ less text in later steps need evaluation

"Multiple dates" usage ? <DATE TYPE="DateDay" D="11" M="2" Y="2008">February 10-13, 2008</DATE> <DATE TYPE="DateDay" D="11" M="2" Y="2008">February 9-13, 2008</DATE> <DATE TYPE="DateDay" D="12" M="11" Y="2007">November 11-13, 2007</DATE> <DATE TYPE="DateDay" D="14" M="10" Y="2008">October 12-17, 2008</DATE> <DATE TYPE="DateDay" D="16" M="2" Y="2009">February 15-18, 2009</DATE> <DATE TYPE="DateDay" D="17" M="9" Y="2007">September 16-19, 2007</DATE> <DATE TYPE="DateDay" D="2" M="5" Y="2008">May 2, 2008</DATE> <DATE TYPE="DateDay" D="26" M="5" Y="2009">May 24-29, 2009</DATE> <DATE TYPE="DateDay" D="27" M="10" Y="2009">October 25-29, 2009</DATE> <DATE TYPE="DateDay" D="7" M="10" Y="2008">October 5-9 2008</DATE> <DATE TYPE="DateDay" D="8" M="2" Y="2009">February 7-10, 2009</DATE> <DATE TYPE="DateDay" D="8" M="5" Y="2007">May 6-11, 2007</DATE> <DATE TYPE="DateDay" D="9" M="10" Y="2007">October 7-12, 2007</DATE> <DATE TYPE="DateMonth" M="11" Y="2009">November, 2009</DATE> <DATE TYPE="DateMonth" M="2" Y="2009">February, 2009</DATE> <DATE TYPE="DateMonth" M="8" Y="2008">August 2008</DATE> retrieve sort by date by date ?

Publishing date ? critical fortime centric searches published tagged as 05/2011 7 jul 2011

& many more… polymorphism sanitize parser output "Nagy Bocsa", "Nagy-Bocsa", "Nagy" for tokenization transliteration, case, punctuation, … wrong compound first names spelling "Jean-Claude Marin"→Claude Marin Tapei→Taipei location is also a first name"University of Michigan, Ann Arbor, MI"→Ann Arbor (person) wrong character case (very frequent on titles) breaks all case-based rules barrack obama→not extracted How To Buy Electric Trucks→Buy Electric (organization) In Virginia Life Is Sweet→Virginia Life (person)

4. Results

Reminder Next results are obtained automatically from unstructured content picked on the web by an autonomous system, without previous knowledgeof the topic or the visited Web sites

Lets try it with a use case"hydrogen storage for fuel cells" run a few cycles some (shallow extraction only) 104 pages entity Whats inside a collection weight functionof 66 highly ranked documents ? (tf-idf, …)Orgs People Location Time

Special attention paid to so-called outliers

Organizations > 900 : overload… page cleaning + entity sanitization => better details & accuracy

↗attention ↘information : top 50 ? H2 military academic usage ? team ?new questions are instantly popping up

People ? authors lead to relevant content(classic IR method,even in libraries !)

Countries political threats on Lithium battery supplies argument in favor of H2 technology

Cities "Direct Methanol Fuel Cells" ⇒alternative to H2 !"Austin is in a unique position ! to offer its electric grid as a real world proving ground" !

Multiple-dates timeline Honda President Takanobu Ito says around 10 percent of Honda’s global sales will be hybrids by 2015domains history outlook changeover from nickel to lithium will be complete by 2016 and 2018 time

In a few clicks...changeover fromnickel to lithium by 2016/2018 hydrogen storage for fuel cells ? DMFC alternative to H2 Austin, TX

5. Perspectives

To clean or not to clean ?corpus run pipeline with/without cleaning full clean time full set set pipeline label examples +/- "attention" impact performance impact

Publishing date extraction heuristic DOM processing prototype readyneed large scale build gold evaluation standard from RSS feeds

A zest of Linked Data ? fat oo s low & .. t ing. for c rawl use it "offline"disambiguation, gazetteers, infoboxes, ...

Play with graphsentity co-occurence, page similarity, ...

UI/user experiencesearch facetsword cloudsmapsdashboardsinfoboxeshighlightinggraphs

Lexical Taxonomies Induction another kind of projection22nd International Joint Conference on Artificial Intelligence (IJCAI 2011), Barcelona, Spain, July 19-22nd, 2011

6. Digesta. A real need of Attention-saving…b. WebNEM results are encouragingc. Work in progress, lots of paths to explore

"Theres simply too muchinformation out there.""Leaders feelmisled. Stupid. Trapped."

Final word by Herbert Simon "Filtering by intelligent programs is the main part of the answer" [to information overload]

MANY THANKS! joint work ofwww.ixxo.frwww.slideshare.net/fpouillouxwww.linkedin.com/pub/st%C3%A9phanie-jacquemont/20/271/767www.linkedin.com/in/fpouilloux

CREDITSPhotos Websites2. Home page, The 2011 IEEE/WIC/ACM International Conference on Web wi-iat-2011.orgIntelligence The Herbert A. Simon Collection, Carnegie Mellon University Libraries,4. Designing Organizations for an Information-Rich World, The Herbert A. diva.library.cmu.edu/webapp/simon/index.htmlSimon Collection www.google.com5.Vlad the Impaler, Wikimedia commons online.barrons.com7. Missile 9M342 of the portable anti-aircraft missile system Igla-S, www.me.utexas.edu/~dmfc-muri©vitalykuzmin.net www.alsace-industrie.fr10. Internet Map 2005, ©www.opte.org www.hybridcars.com33. The Inspector, ©DePatie-Freleng Enterprises www.me.utexas.edu/blogs/meyersresearchgroup36. Nanomaterials for Solid State Hydrogen Storage, book cover,©springer.com40. EnerDel/Argonne lithium-ion battery, ©Argonne National Laboratory40. Pennybacker Bridge - Austin, TX, ©Andy Heatwole Bibliography Simon, H. A. (1971), "Designing Organizations for an Information-Rich41. 20060206211301_132363.jpg, pulpo.org, ©Jumpedforjoy World", Carnegie Mellon University Libraries,44. Linking Open Data cloud diagram, ©Richard Cyganiak and Anja diva.library.cmu.edu/webapp/simon/item.jsp?q=/box00055/fld04178/bdlJentzsch, lod-cloud.net 0002/doc000144. Taji crawl, ©The U.S. Army, www.flickr.com/soldiersmediacenter Waters, T. J. (2011), "Hyperformance",48. Views of the solar corona by the Transition Region and Coronal www.tjwaters.com/hyperformance-excerpt.htmlExplorer, Stanford-Lockheed Institute for Space Research, NASA Small R. Navigli, P. Velardi, S. Faralli. A Graph-based Algorithm for InducingExplorer program Lexical Taxonomies from Scratch. Proc. of the 22nd International Joint49. Hyperformance book cover, www.tjwaters.com Conference on Artificial Intelligence (IJCAI 2011), Barcelona, Spain, July50. Dr Simon solving puzzles, The Herbert A. Simon Collection 19-22nd, 2011, pp. 1872-1877.

Web Scale Named Entity Mining

by Francois Pouilloux on Aug 24, 2011

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