Generalized Vector Space Models (GVSM) extend the standard Vector Space Model (VSM) by embedding additional types of information, besides terms, in the representation of documents. An interesting type of information that can be used in such models is semantic information from word thesauri like WordNet. Previous attempts to construct GVSM reported contradicting results. The most challenging problem is to incorporate the semantic information in a theoretically sound and rigorous manner and to modify the standard interpretation of the VSM. In this paper we present a new GVSM model that exploits WordNet’s semantic information. The model is based on a.
