Package tml.vectorspace

Implements a Vector Space Model, that can be later transformed using Latent Semantic Analysis.

See:
Description

Class Summary

SemanticSpace	This class is a Vector Space Model representation of a group of documents or Corpus constructed using Latent Semantic Indexing, it contains a term by document matrix for the Corpus.
SVD
TermWeighting	The TermWeighting filter transforms a basic Term/Document matrix to a different Term/Weighting scheme.

Enum Summary

TermWeighting.GlobalWeight	Implemented global weight functions
TermWeighting.LocalWeight	Implemented local weight functions

Exception Summary

EmptyTextPassageException	This class implements an identifiable exception when a TextPassage contains no words
NoDocumentsInCorpusException	Exception raised when no documents are found in the Corpus
NotEnoughTermsInCorpusException	This class represents an exception thrown when the Corpus does not contain enough terms, therefore the SVD decomposition can't be performed
TermWeightingException	Exception occurred while applying the term weighting criteria

Package tml.vectorspace Description

Implements a Vector Space Model, that can be later transformed using Latent Semantic Analysis.

Package Specification

This package implements the transformation of a Corpus into a VSM, it also implements the possibility of using LSA to obtain a Semantic Space.

The package is closely integrated with Weka, providing Data Mining functionalities in case a developer wants operations that are not implemented in TML.

Patterns that can be obtained from a VSM or semantic space are implemented via operations, that can be found in the operations subpackage.