The Topic-based Vector Space Model (TVSM) (literature

extends the

vector space model Vector space model or term vector model is an algebraic model for representing text documents (or more generally, items) as vector space, vectors such that the distance between vectors represents the relevance between the documents. It is used in i ...

information retrieval Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an Information needs, information need. The information need can be specified in the form ...

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.

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

is a proposal on how to derive term vectors from an Ontology_(information_science) , Ontology. Using a synonym Ontology created from

WordNet WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into ''synsets'' with short definitions and usage examples. It can thu ...

Kuropka shows good results for document similarity. If a trivial Ontology is used the results are similar to Vector Space model.

Implementations

Implementation of eTVSM in python

References

{{reflist Vector space model