Characterizing Large Text Corpora Using a Maximum Variation Sampling Genetic Algorithm

Conference · Sat Dec 31 23:00:00 EST 2005

DOI:https://doi.org/10.1145/1143997.1144308· OSTI ID:931452

Patton, Robert M ^[1]

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1. ORNL

An enormous amount of information available via the Internet exists. Much of this data is in the form of text-based documents. These documents cover a variety of topics that are vitally important to the scientific, business, and defense/security communities. Currently, there are a many techniques for processing and analyzing such data. However, the ability to quickly characterize a large set of documents still proves challenging. Previous work has successfully demonstrated the use of a genetic algorithm for providing a representative subset for text documents via adaptive sampling. In this work, we further expand and explore this approach on much larger data sets using a parallel Genetic Algorithm (GA) with adaptive parameter control. Experimental results are presented and discussed.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 931452
• Country of Publication: United States
• Language: English