It mainly relies on the quantitative analysis of the glosses associated to synsets, and on the use of the resulting vector space model representations for semisupervised synset classification.
temporal classifiers are learned over manually labeled synsets (seed list), and new learning synsets are chosen based on their specific semantic relation (e.g.