Niching methods for genetic algorithms
Niching methods for genetic algorithms
A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving text categorization methods for event tracking
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Application of intelligent methods for improved testing and evaluation of military simulation software
Complexity of event structure in IE scenarios
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Adaptive information extraction
ACM Computing Surveys (CSUR)
TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
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Automated event extraction remains a very difficult challenge requiring information analysts to manually identify key events of interest within massive, dynamic data. Many techniques for extracting events rely on domain specific natural language processing or information retrieval techniques. As an alternative, this work focuses on detecting events based on identifying event characteristics of interest to an analyst. An evolutionary algorithm is developed as a proof of concept to demonstrate this approach. Initial results indicate that this approach represents a feasible approach to identifying critical event information in a massive data set with no apriori knowledgeof the data set.