Topic-based vector space model

The basic premise of TVSM is the existence of a d dimensional space R with only positive axis intercepts, i.e. R in R⁺ and d in N⁺. Each dimension of R represents a fundamental topic. A term vector t has a specific weight for a certain R. To calculate these weights assumptions are made taking into account the document contents. Ideally important terms will have a high weight and stopwords and irrelevants terms to the topic will have a low weight. The TVSM document model is obtained as a sum of term vectors representing terms in the document. The similarity between two documents Di and Dj is defined as the scalar product of document vectors.

The enhancement of the Enhanced Topic-based Vector Space Model (eTVSM) (literature: ) is a proposal on how to derive term vectors from an Ontology. Using a synonym Ontology created from WordNet Kuropka shows good results for document similarity. If a trivial Ontology is used the results are similar to Vector Space model.