Neural networks for pattern recognition
Neural networks for pattern recognition
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data Mining Approaches to Criminal Career Analysis
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fuzzy Indices of Document Reliability
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
A flexible multi criteria information filtering model
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Soft Computing on Web; Guest Editors: A. G. López-Herrera, E. Herrera-Viedma
Possibilistic logic bases and possibilistic graphs
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
ArabOnto: experimenting a new distributional approach for building Arabic ontological resources
International Journal of Metadata, Semantics and Ontologies
Arabic morphological analysis and disambiguation using a possibilistic classifier
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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The Arabic storytelling methodology provides solutions to the problem of information reliability. The reliability of a story depends on the credibility of its narrators. To insure reliability verification, the narrators' names are explicitly cited at the head of the story, which constitute its chain of narrators. Stories were reported from a generation to another to insure the reliable transmission of historical knowledge. We present a set of tools based on the Arabic storytelling methodology. We start by presenting this methodology as a set of principles for information-reliability assessment. Then, we detail an architecture designed to support the study of the reliability of Arabic stories. Indeed, we developed grammars for parsing Arabic full names and chains of narrators of Arabic stories. After that, an intelligent identity recognizer links names found in chains of narrators to the biographies of the corresponding persons. We model this step as a possibilistic information retrieval task. Finally, chains are analyzed through metadata available in biographies to help the user identify sources of unreliability. We propose to identify the class of reliability of a story with a possibilistic classifier. The achieved results in named entity and identity recognition were satisfactory and confirm to the targets set for the precision, recall, and F-measure metrics. The developed tools also are reusable components that can be used to study the reliability of other types of Arabic texts. © 2010 Wiley Periodicals, Inc.