Computing & Information Sciences Kansas State University University of Kansas Center for Business Analytics research Seminar Laboratory for Knowledge Discovery in Databases University of Kansas CBAR Wednesday, 04 September 2013 William H. Hsu Laboratory for Knowledge Discovery in Databases, Kansas State University http://www.kddresearch.org Acknowledgements Kansas State: Wesam Elshamy, Ming Yang, Surya Teja Kallumadi, Majed Alsadhan Illinois: Chengxiang Zhai, Jiawei Han, Kevin Chang, Dan Roth iQGateway: Praveen Koduru, Krishna Kumar Vallyatodi Dynamic Topic Modeling for Spatiotemporal Event Extraction: Probabilistic Approaches and The Dim Sum Process
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Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
University of Kansas CBAR
Wednesday, 04 September 2013
William H. HsuLaboratory for Knowledge Discovery in Databases, Kansas State University
http://www.kddresearch.org
Acknowledgements
Kansas State: Wesam Elshamy,
Ming Yang, Surya Teja Kallumadi, Majed Alsadhan
Illinois: Chengxiang Zhai, Jiawei Han, Kevin Chang, Dan Roth
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Volkova, S., Caragea, D., Hsu, W. H., Drouhard, J., & Fowles, L. (2010). Boosting Biomedical Entity Extraction by using Syntactic Patterns for Semantic Relation Discovery. Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2010).
See also: Volkova, S. (2010). As Entity Extraction, Animal Disease-related Event Recognition and Classification from Web. M.S. thesis, Kansas State University.
Motivation: Thematic Mapping [5]Existing Systems & Limitations
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Simultaneous Topic Enumeration & Formation
Topic Modeling: Static (Atemporal) to Dynamic
Continuous Time vs. Variable Number of Topics
Dim Sum Process for Hybrid STEF
Dynamic Topic Modeling Test Bed
News Monitoring: Geotagging & Timelines
Recent Results
STEF & Heterogeneous Info Network Analysis
OutlineOutline
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Timeline Formation:General Task Illustrated
Elshamy (2012)
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Simultaneous Topic Enumeration& Formation (STEF)
Adapted from Elshamy (2012)
Time t: 3 extant topics Time t + k: 2 extant topics
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Simultaneous Topic Enumeration & Formation
Topic Modeling: Static (Atemporal) to Dynamic
Continuous Time vs. Variable Number of Topics
Dim Sum Process for Hybrid STEF
Dynamic Topic Modeling Test Bed
News Monitoring: Geotagging & Timelines
Recent Results
STEF & Heterogeneous Info Network Analysis
OutlineOutline
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Topic Modeling [1]:Basic Task (Static)
Elshamy (2012)
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Topic Modeling [2]:Understanding Plate Notation
Adapted from Elshamy (2012)
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Simultaneous Topic Enumeration & Formation
Topic Modeling: Static (Atemporal) to Dynamic
Continuous Time vs. Variable Number of Topics
Dim Sum Process for Hybrid STEF
Dynamic Topic Modeling Test Bed
News Monitoring: Geotagging & Timelines
Recent Results
STEF & Heterogeneous Info Network Analysis
OutlineOutline
Computing & Information SciencesKansas State University
University of KansasCenter for Business Analytics research Seminar
Laboratory forKnowledge Discovery in Databases
Thematic Mapping Tasks [1]:Entities
Example: CNN, 2007 Foot-and-Mouth Disease (http://bit.ly/3gof6o) Tests have confirmed a second foot-and-mouth outbreak in southern England, the government announced, raising fears that the highly contagious animal virus is spreading.
Chief Veterinary Officer Debby Reynolds said Tuesday that tests showed a herd of cattle had been infected.
The animals were culled Monday evening after showing signs of the disease.
Update SummarizationA second foot-and-mouth disease infection in a herd of cattle in southern England was responded to by culling on Monday evening and announced by Debby Reynolds on Tuesday.
