Topic-based vector space model
Encyclopedia
The Topic-based Vector Space Model (TVSM) (literature: http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=0514&lng=eng&id=) extends the Vector space model
by removing the constraint that the term-vectors be orthogonal. The assumption of orthogonal terms is incorrect regarding natural languages which causes problems with synonyms and strong related terms. This facilitates the use of stopword lists, stemming and thesaurus in TVSM.
In contrast to the Generalized vector space model
the TVSM does not depend on concurrence-based similarities between terms.
Kuropka shows good results for document similarity. If a trivial Ontology is used the results are similar to Vector Space model.
Vector space model
Vector space model is an algebraic model for representing text documents as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings...
by removing the constraint that the term-vectors be orthogonal. The assumption of orthogonal terms is incorrect regarding natural languages which causes problems with synonyms and strong related terms. This facilitates the use of stopword lists, stemming and thesaurus in TVSM.
In contrast to the Generalized vector space model
Generalized vector space model
The Generalized vector space model is a generalization of the vector space model used in information retrieval. Wong et al. presented an analysis of the problems that the pairwise orthogonality assumption of the Vector space model creates...
the TVSM does not depend on concurrence-based similarities between terms.
Definitions
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.Enhanced Topic-based Vector Space Model
The enhancement of the Enhanced Topic-based Vector Space Model (eTVSM) (literature: http://www.logos-verlag.de/cgi-bin/engbuchmid?isbn=0514&lng=eng&id=) is a proposal on how to derive term vectors from an Ontology. Using a synonym Ontology created from WordNetWordNet
WordNet is a lexical database for the English language. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets...
Kuropka shows good results for document similarity. If a trivial Ontology is used the results are similar to Vector Space model.