org.carrot2.text.vsm
Class TermDocumentMatrixBuilder

java.lang.Object
  org.carrot2.text.vsm.TermDocumentMatrixBuilder

public class TermDocumentMatrixBuilder
extends Object

Builds a term document matrix based on the provided PreprocessingContext.

Field Summary

int	maximumMatrixSize Maximum matrix size.
double	maxWordDf Maximum word document frequency.
ITermWeighting	termWeighting Term weighting.
double	titleWordsBoost Title word boost.

Constructor Summary

TermDocumentMatrixBuilder()

Method Summary

void	buildTermDocumentMatrix(VectorSpaceModelContext vsmContext) Builds a term document matrix from data provided in the context, stores the result in there.
void	buildTermPhraseMatrix(VectorSpaceModelContext context) Builds a term-phrase matrix in the same space as the main term-document matrix.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

titleWordsBoost

public double titleWordsBoost

Title word boost. Gives more weight to words that appeared in Document.TITLE fields.

Attribute label:

Title word boost

Attribute level:

Medium

Attribute group:

Labels

maximumMatrixSize

public int maximumMatrixSize

Maximum matrix size. The maximum number of the term-document matrix elements. The larger the size, the more accurate, time- and memory-consuming clustering.

Attribute label:

Maximum matrix size

Attribute level:

Medium

Attribute group:

Matrix model

maxWordDf

public double maxWordDf

Maximum word document frequency. The maximum document frequency allowed for words as a fraction of all documents. Words with document frequency larger than maxWordDf will be ignored. For example, when maxWordDf is 0.4, words appearing in more than 40% of documents will be be ignored. A value of 1.0 means that all words will be taken into account, no matter in how many documents they appear.

This attribute may be useful when certain words appear in most of the input documents (e.g. company name from header or footer) and such words dominate the cluster labels. In such case, setting maxWordDf to a value lower than 1.0, e.g. 0.9 may improve the clusters.

Another useful application of this attribute is when there is a need to generate only very specific clusters, i.e. clusters containing small numbers of documents. This can be achieved by setting maxWordDf to extremely low values, e.g. 0.1 or 0.05.

Attribute label:

Maximum word document frequency

Attribute level:

Advanced

Attribute group:

Matrix model

termWeighting

public ITermWeighting termWeighting

Term weighting. The method for calculating weight of words in the term-document matrices.

Attribute label:

Term weighting

Attribute level:

Advanced

Attribute group:

Matrix model

Constructor Detail

TermDocumentMatrixBuilder

public TermDocumentMatrixBuilder()

Method Detail

buildTermDocumentMatrix

public void buildTermDocumentMatrix(VectorSpaceModelContext vsmContext)

Builds a term document matrix from data provided in the context, stores the result in there.

buildTermPhraseMatrix

public void buildTermPhraseMatrix(VectorSpaceModelContext context)

Builds a term-phrase matrix in the same space as the main term-document matrix. If the processing context contains no phrases, VectorSpaceModelContext.termPhraseMatrix will remain null.