Interesting reading follows:
++ UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic Features

We describe the SuperSenseLearner system that participated in the English allwords disambiguation task. The system relies on automatically-learned semantic models using collocational features coupled with features extracted from the annotations of coarse-grained semantic categories generated by an HMM tagger.

by R. Mihalcea, A. Csomai and M. Ciaramita (2007)
Source: Proceedings of SemEval 2007 Workshop (ACL 2007)

and

Semantic Domains and Supersense Tagging for Domain-Specific Ontology Learning
6 pages; PDF

In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses,providing flat ontologies characterized by very high accuracy and pertinence to the domain.

Source: Proceedings of RIAO 2007.

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