'Text mining' presentation slideshows

Text mining - PowerPoint PPT Presentation


Components of an effective anti-fraud & corruption compliance program

Components of an effective anti-fraud & corruption compliance program

Anti-Fraud Trends and Analytics Integrating anti-bribery & corruption analytics into your compliance monitoring program Meeting with Verizon October 19, 2012. Components of an effective anti-fraud & corruption compliance program. Setting the Proper Tone. Proactive. Reactive.

By rex
(1237 views)

Research Team

Research Team

Automatic Extraction of Mental Illnesses from Domestic Violence Event Narratives: a Text Mining Study. George Karystianis , PhD Research Fellow, Justice Health Research Program, Kirby Institute, University of New South Wales. Research Team.

By avinoam
(270 views)

Text Mining Chapter 20

Text Mining Chapter 20

Text Mining Chapter 20. Text data . Structured data Unstructured data Text Video Audio. Applications of Text Mining – HR Forms. Employment applications Match with job requirements Processing of applications. Applications, cont. Medical triage/diagnosis

By orly
(518 views)

User

User

User. Warehouse. Data Warehouse. DBMS A. DBMS B. DBMS C. Database. Database. Database. Data warehouse example. User. Data Mining Tools. Warehouse. Data Warehouse Manager. DBMS C. DBMS A. DBMS B. Database. Database. Database.

By trella
(203 views)

Text Mining Concepts

Text Mining Concepts

Text Mining Concepts. 85-90 percent of all corporate data is in some kind of unstructured form (e.g., text) Unstructured corporate data is doubling in size every 18 months Tapping into these information sources is not an option, but a need to stay competitive Answer: text mining

By kanoa
(310 views)

Mixture Language Models

Mixture Language Models

Mixture Language Models. ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Central Questions to Ask about a LM: “ADMI”. Application : Why do you need a LM? For what purpose? Data: What kind of data do you want to model?

By helki
(91 views)

Going beyond Turnitin and Plagiarism Serge Noiret EUI History Information Specialist (Ph.D.)

Going beyond Turnitin and Plagiarism Serge Noiret EUI History Information Specialist (Ph.D.)

Going beyond Turnitin and Plagiarism Serge Noiret EUI History Information Specialist (Ph.D.) Introduction to Good Academic Practice and the Avoidance of Plagiarism organized by Prof. Lucy Riall, Prof. Stéphane Van Damme and Dr. Serge Noiret Tuesday 8 October 2013, 13:10-15:00

By levana
(105 views)

TEXT ANALYSIS FOR SEMANTIC COMPUTING

TEXT ANALYSIS FOR SEMANTIC COMPUTING

CARTIC RAMAKRISHNAN MEENAKSHI NAGARAJAN AMIT SHETH. TEXT ANALYSIS FOR SEMANTIC COMPUTING. Preparing for a Tutorial. A Great way to find out…. How little you really know. Acknowledgements.

By julius
(0 views)

A DoD DCMO Enterprise Information Web

A DoD DCMO Enterprise Information Web

A DoD DCMO Enterprise Information Web. Dr. Brand Niemann Director and Senior Enterprise Architect – Data Scientist Semantic Community http://semanticommunity.info/ AOL Government Blogger http://gov.aol.com/bloggers/brand-niemann/ February 6, 2012

By cain
(174 views)

Analytics in Strategic Decision Making Brazil Executive Seminar, April 2014

Analytics in Strategic Decision Making Brazil Executive Seminar, April 2014

Analytics in Strategic Decision Making Brazil Executive Seminar, April 2014. PRESENTED BY DR. FAIZUL HUQ OHIO UNIVERSITY. AGENDA. Introduction to Business Analytics. Example of Analytical Tool Implementation for Supply Chain Sustainability How to make Analytics work for Strategic Success

By moanna
(102 views)

Social Network Analysis: Concepts, Applications, Analysis of Social Structures Using Secondary Data

Social Network Analysis: Concepts, Applications, Analysis of Social Structures Using Secondary Data

Social Network Analysis: Concepts, Applications, Analysis of Social Structures Using Secondary Data. Liaquat Hossain Sciences of Learning Winter Institute Workshop The University of Hong Kong Hong Kong, 14 January 2014 Email: lhossain@hku.hk. Agenda. Introduction to Social Networks

By colm
(274 views)

Computation + journalism in the public interest

Computation + journalism in the public interest

Computation + journalism in the public interest. Sarah Cohen, DeWitt Wallace Center. Public interest reporting defined. Information of importance to the public that powerful institutions would prefer to be kept hidden or secret.

By samson
(85 views)

Improved Cancer Risk Assessment Using Text Mining

Improved Cancer Risk Assessment Using Text Mining

Literature Database (e.g. MEDLINE). Journal articles for cancer risk assessment. Cancer risk assessment taxonomy. Annotation of cancer risk assessment corpus. Text Mining technology. Identify chemicals’ ”modes of action”. Text mining tool.

By orinda
(105 views)

Computational Modeling of Hepatic Lipid and Lipoprotein Metabolism

Computational Modeling of Hepatic Lipid and Lipoprotein Metabolism

Computational Modeling of Hepatic Lipid and Lipoprotein Metabolism. NCSB / NBIC Modelers-Bioinformatics Meeting 26-02-2010. Leiden University Medical Center. BioModeling & bioInformatics. Biomedical NMR. Overall aim:

By carlyn
(215 views)

IBM SPSS Modeler

IBM SPSS Modeler

IBM SPSS Modeler. Ercan Kaynakca Data Analyst Analysis Express. Knowledge Discovery. Fayyad, Piatetsky -Shapiro & Smyth, 1996. Why Data Mining?. Goal : Extract knowledge from a data set in a human-understandable structure (Fayyad, Piatetsky -Shapiro & Smyth, 1996 ).

By marrim
(746 views)

PAnG – Finding Patterns in Annotation Graphs

PAnG – Finding Patterns in Annotation Graphs

PAnG – Finding Patterns in Annotation Graphs. Philip Anderson, Andreas Thor, Joseph Benik , Louiqa Raschid, Maria Esther Vidal. Motivation. Approach. Abundance of data High-throughput lab experiments in systems biology. Annotated datasets adorned with CV terms from ontologies .

By cherie
(146 views)

OntoSTUDIO as a Ontology Engineering Environment

OntoSTUDIO as a Ontology Engineering Environment

OntoSTUDIO as a Ontology Engineering Environment. Presented By Stephen Lynn Department of Computer Science Brigham Young University. Semantic Web Tools. Tasks Ontology Creation Manual Learning Reasoning Text Mining Etc. Practical Data Storage Metadata Storage. State of the Art.

By ivo
(163 views)

Text mining : Finding nuggets in mountains of textual data

Text mining : Finding nuggets in mountains of textual data

Author : Jochen Dijrre , Peter Gerstl , Roland Seiffert Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , San Diego, California, August 15-18, 1999, 398-401. Presented by Xxxxxx. Text mining : Finding nuggets in mountains of textual data.

By iria
(138 views)

Presenter : Wei- Hao Huang

Presenter : Wei- Hao Huang

Discovering Interesting Usage Patterns in Text Collections: Integrating Text Mining with Visualization. Presenter : Wei- Hao Huang

By ania
(136 views)

Link Analysis: Current State of the Art

Link Analysis: Current State of the Art

Link Analysis: Current State of the Art. Ronen Feldman Computer Science Department Bar-Ilan University, ISRAEL ronenf@gmail.com. Introduction to Text Mining. Actual information buried inside documents. Extract Information from within the documents. TM != Search.

By binta
(109 views)

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