We propose a method which, given a document to be classified, automatically generates an ordered set of appropriate descriptors extracted from a thesaurus. The method creates a Bayesian network to model the thesaurus and uses probabilistic inference to select the set of descriptors having high posterior probability of being relevant given the available evidence (the document to be classified). Our model can be used without having preclassified training documents, although it improves its performance as long as more training data become available. We have tested the classification model using a document dataset containing parliamentary resolutions from the regional Parliament of Andalucía at Spain, which were manually indexed from the Eurovoc thesaurus, also carrying out an experimental comparison with other standard text classifiers. Crown
REF automatically associated an ordered set of appropriate descriptors extracted from EuroVoc to a document to be classified creating a unique Bayesian network for the full thesaurus; this approach can be used with or without training data.
Bayesian network models for hierarchical text classification from a thesaurus
Int. J. Approx. Reason.
Int. J. Approx. Reason.
Computer Science