Research on Ontology-driven Text Virtual Sample Constructing
Xiao Wang · 2008
Constructing virtual examples can incorporate prior knowledge into training set in machine learning,so as to alleviate the labeling bottleneck. An Ontology-driven scheme to construct text virtual sample is proposed. Under the precondition of label invariability,the proposal constructs virtual samples according to the domain knowledge explicitly formalized by domain-specific Ontology. Based on the different Ontology tree structures,namely nodes,edges,and sub-trees,various lexical-semantic relations,including synonymy,paternity,and semantic isomorphs,are applied into text virtual example constructing. The primary experimental results show the scheme outperforms original text categorizations and other similar ones in precision and generalization ability.