Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Mining e-mail content for author identification forensics
ACM SIGMOD Record
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Extracting meaningful entities from police narrative reports
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Learning Bayesian Networks
Bayesian information extraction network
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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The feasibility of a probabilistic Named Entity Recognition system in a South African context was tested. The intended use of the system is in a cyber forensic domain. At the core of the system is a dynamic Bayesian Network, which takes into account the probabilistic relationship between variables as well as contextual information. We illustrate the performance of such a system using different probability thresholds for classification purposes and compare the performance with and without a name gazetteer. Our system compares competently with similar existing systems in the information extraction domain. Future work will involve the application of the system in the cyber forensic environment, which poses new challenges such as diverse text types.