Domain Representation Using Possibility Theory: An Exploratory Study

This study explores a new domain representation method for natural language processing based on an application of possibility theory. In our method, domain-specific information is extracted from natural language documents using a mathematical process based on Rieger's notion of semantic distances, and represented in the form of possibility distributions. We implement the distributions in the context of a possibilistic domain classifier, which is trained using the SchoolNet corpus.