org.terrier.matching.models.queryexpansion

Class KL

java.lang.Object

org.terrier.matching.models.queryexpansion.QueryExpansionModel

org.terrier.matching.models.queryexpansion.KL

public class KL
extends QueryExpansionModel

This class implements the Kullback-Leibler divergence for query expansion. See G. Amati's PhD Thesis.

Author:

Gianni Amati, Ben He

Field Summary

Fields inherited from class org.terrier.matching.models.queryexpansion.QueryExpansionModel

averageDocumentLength, collectionLength, documentFrequency, EXPANSION_DOCUMENTS, EXPANSION_TERMS, idf, maxTermFrequency, numberOfDocuments, PARAMETER_FREE, ROCCHIO_BETA, totalDocumentLength

Constructor Summary

Constructors

Constructor and Description
KL() A default constructor.

Method Summary

Methods

Modifier and Type	Method and Description
String	getInfo() Returns the name of the model.
double	parameterFreeNormaliser() This method computes the normaliser of parameter-free query expansion.
double	parameterFreeNormaliser(double maxTermFrequency, double collectionLength, double totalDocumentLength) This method computes the normaliser of parameter-free query expansion.
double	score(double withinDocumentFrequency, double termFrequency) This method implements the query expansion model.
double	score(double withinDocumentFrequency, double termFrequency, double totalDocumentLength, double collectionLength, double averageDocumentLength) This method implements the query expansion model.

Methods inherited from class org.terrier.matching.models.queryexpansion.QueryExpansionModel

initialise, setAverageDocumentLength, setCollectionLength, setDocumentFrequency, setMaxTermFrequency, setNumberOfDocuments, setTotalDocumentLength

Methods inherited from class java.lang.Object

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

Constructor Detail

KL

public KL()

A default constructor.

Method Detail

getInfo

public final String getInfo()

Returns the name of the model.

Specified by:

getInfo in class QueryExpansionModel

Returns:

the name of the model

parameterFreeNormaliser

public final double parameterFreeNormaliser()

This method computes the normaliser of parameter-free query expansion.

Specified by:

parameterFreeNormaliser in class QueryExpansionModel

Returns:

The normaliser.

parameterFreeNormaliser

public final double parameterFreeNormaliser(double maxTermFrequency,
                             double collectionLength,
                             double totalDocumentLength)

This method computes the normaliser of parameter-free query expansion.

Specified by:

parameterFreeNormaliser in class QueryExpansionModel

Parameters:

maxTermFrequency - The maximum of the term frequency of the query terms.

collectionLength - The number of tokens in the collections.

totalDocumentLength - The sum of the length of the top-ranked documents.

Returns:

The normaliser.

score

public final double score(double withinDocumentFrequency,
           double termFrequency)

This method implements the query expansion model.

Specified by:

score in class QueryExpansionModel

Parameters:

withinDocumentFrequency - double The term frequency in the X top-retrieved documents.

termFrequency - double The term frequency in the collection.

Returns:

double The query expansion weight using he complete Kullback-Leibler divergence.

score

public final double score(double withinDocumentFrequency,
           double termFrequency,
           double totalDocumentLength,
           double collectionLength,
           double averageDocumentLength)

This method implements the query expansion model.

Specified by:

score in class QueryExpansionModel

Parameters:

withinDocumentFrequency - double The term frequency in the X top-retrieved documents.

termFrequency - double The term frequency in the collection.

totalDocumentLength - double The sum of length of the X top-retrieved documents.

collectionLength - double The number of tokens in the whole collection.

averageDocumentLength - double The average document length in the collection.

Returns:

double The score returned by the implemented model.