General Motors
BP
Ford Motor Company
AstraZeneca
DaimlerChrysler
CNN
General Electric
US Senate
Credit Suisse First Boston
Volkswagen
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The McGraw-Hill Companies
Philip Morris
Lloyds
Hewlett Packard
Bloomberg
Verizon
AT&T
FIAT
US Department of Defense
UK Department of Trade & Industry
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Philips
General Dynamics
Sun Microsystems
Hewlett Packard
ABN Amro
Macmillan Publishing
Sun Microsystems
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UBS Warburg
Merrill Lynch
Philips
BP
New York Stock Exchange
The Economist
France Telecom
Swiss Army
Boeing
Lafarge
Safeway
ABN Amro
AT&T
People's Republic of China's
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AstraZeneca
Nestle
Nordea
MOL
Tesco
Nestle
New York Life Insurance
Pfizer
BBC
Philips
AstraZeneca
3
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Lloyds
Canon USA
Novell
Ericsson
EDS
Philip Morris International
Royal & SunAlliance
Novartis
Credit Lyonnais
Sun Microsystems
British American Tobacco
Norsk Hydro
AstraZeneca
Skanska
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Royal & SunAlliance
BAE Systems
Kodak
The Royal Mail Group
AstraZeneca
Henkel
Bank of Montreal
Danske Bank
BMW
Kronos Corporation
Fujitsu Technology Services
Zurich Financial Services
Halliburton
Lloyds
BBC
Blue Cross/Blue Shield of Massachusetts
T-Mobile
Channel 4 Corporation
VHA
Burges Salmon
Motorola
British Telecom
3
Ferrari
Philips
AT&T
3
Deloitte & Touche
HM Revenue & Customs
PA Consulting
US Army
UK Department of Trade & Industry
EMC Corporation
US Department of Commerce
Encana Corporation
IEEE
Hewitt Associates LLC
HEALTHvision
General Motors
US State Department
Paramount
Lexmark
US Department of Defense
JD Edwards
Ingersoll-Rand
AstraZeneca
PricewaterhouseCoopers
Credit Lyonnais
Vodafone Omnitel
Nomura
US State Department
3
Danske Bank
Reed Elsevier
Dow Chemical Company
Siemens Power Generation
Texas Instruments
Forrester Research
T-Mobile
McData
3
Wall Street Journal
US State Department
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NASA
SCA
Reuters
ITN
IBM NICA
Forbes.com
Nissan North America, Inc.
Toyota Motor
The McGraw-Hill Companies
Fox Sports
Society of Petroleum Engineers
Ericsson
US Department of Energy
European Commission
Telecom Italia
Harrah's
AXA
Sybase
Napster
3
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Compuware
Olympus
ARM
General Motors
BP
Taylor & Francis
Federal Express
Nissan Motor
Britvic Softdrinks
AstraZeneca
Milward Brown Precis
Federal Government of Canada
US Department of Commerce
UK Home Office
HM Revenue & Customs
Sun Microsystems
3
Harvard Business School
Britvic Softdrinks
Hewlett Packard
MOL
Macmillan Publishing
Allianz Life Insurance Co
Swiss Army
US Department of Defense
Parliament of Singapore
Ericsson
General Motors
VMS
Nestle
Singapore Police Force
Sony Music
UK Department of Trade & Industry
Siemens
GSA Advantage!
US State Department
Ingersoll-Rand
Philips
Kaiser Permanente
Stanford Business School
Johns Hopkins
Vodafone Omnitel
Wachovia
General Motors
Standard Life Insurance
Raytheon
Commerzbank
Pfizer
Allstate Insurance
Siemens
State of Washington
BP
Napa Valley County
BBC
Texas Department of Transportation
American HomePatient
TIBCO
Sharper Image
Xerox
America Online
Lockheed Northrop Grumman
Dow Chemical Company
Draeger Medical
Nestle
Sutter Health
Henkel
Kenyan AIDS Clinic
University of Washington
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World Wildlife Fund
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Functionality Sentiment Analysis | IDOL Eduction | Content Rationalization
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IDOL Eduction

IDOL goes beyond traditional entity extraction and enriches the extracted data based on the knowledge already held within the organization. Not only can IDOL extract specific data such as organizations, people, places and figures, it can conceptually relate this information to other data held within the enterprise and automatically form relationships. Eduction comes with outof- the-box entities including: names of commercial organizations, people, places, postal/Internet addresses, phone numbers, dates, times, numbers, prices, Social Security numbers, job titles and holidays. A useful application of the technology includes extracting key information from resumes.

Scenario 1: Recruitment

Different people structure their resumés very differently, putting key information such as contact details, qualifications and previous experience in different locations on the page. IDOL Eduction can be used to automatically identify and extract these key pieces of information and populate a coherent database of candidates.

Scenario 2: Suspected Terrorist Monitoring

IDOL Eduction can be applied to monitor radio chatter between suspected terrorists. By identifying and extracting those entities that relate to geographic locations, the system can alert authorities when a new location is mentioned for the first time.

Key Benefits

Automatically Populate Structured Fields
Understand Conceptual Relevance
Rapid, Accurate Extraction of Data
Automatically Extract Metadata to Enrich Search
Minimize Administrative Overheads
Format and Language Independent
Further Reference: PDF Icon IDOL Eduction Product Brief

This is a selection of our forthcoming events, please visit our seminars page for more information.

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This is a small selection of the Autonomy case studies available, please visit our publications site at http://publications.autonomy.com/ for more information.

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Functionality Sentiment Analysis | IDOL Eduction | Content Rationalization
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