Class KLComplete
- java.lang.Object
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- org.terrier.matching.models.queryexpansion.QueryExpansionModel
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- org.terrier.matching.models.queryexpansion.KLComplete
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public class KLComplete extends QueryExpansionModel
This class implements the complete Kullback-Leibler divergence for query expansion. See the equation after (8.12) for query expansion, page 149.- Author:
- Gianni Amati, Ben He, Vassilis Plachouras
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Field Summary
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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
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Constructor Summary
Constructors Constructor Description KLComplete()
A default constructor.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description java.lang.String
getInfo()
Returns the name of the model.double
parameterFreeNormaliser()
This method provides the contract for computing the normaliser of parameter-free query expansion.double
parameterFreeNormaliser(double maxTermFrequency, double collectionLength, double totalDocumentLength)
This method provides the contract for computing the normaliser of parameter-free query expansion.double
score(double withinDocumentFrequency, double termFrequency)
This method implements the complete Kullback-Leibler divergence for query expansion.double
score(double withinDocumentFrequency, double termFrequency, double totalDocumentLength, double collectionLength, double averageDocumentLength)
This method provides the contract for implementing query expansion models.-
Methods inherited from class org.terrier.matching.models.queryexpansion.QueryExpansionModel
initialise, setAverageDocumentLength, setCollectionLength, setDocumentFrequency, setMaxTermFrequency, setNumberOfDocuments, setTotalDocumentLength
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Method Detail
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getInfo
public final java.lang.String getInfo()
Returns the name of the model.- Specified by:
getInfo
in classQueryExpansionModel
- Returns:
- the name of the model
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parameterFreeNormaliser
public final double parameterFreeNormaliser()
This method provides the contract for computing the normaliser of parameter-free query expansion.- Specified by:
parameterFreeNormaliser
in classQueryExpansionModel
- Returns:
- The normaliser.
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parameterFreeNormaliser
public final double parameterFreeNormaliser(double maxTermFrequency, double collectionLength, double totalDocumentLength)
This method provides the contract for computing the normaliser of parameter-free query expansion.- Specified by:
parameterFreeNormaliser
in classQueryExpansionModel
- Parameters:
maxTermFrequency
- The maximum of the in-collection term frequency of the terms in the pseudo relevance set.collectionLength
- The number of tokens in the collections.totalDocumentLength
- The sum of the length of the top-ranked documents.- Returns:
- The normaliser.
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score
public final double score(double withinDocumentFrequency, double termFrequency)
This method implements the complete Kullback-Leibler divergence for query expansion. See the equation after (8.12) for query expansion, page 149.- Specified by:
score
in classQueryExpansionModel
- 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.
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score
public final double score(double withinDocumentFrequency, double termFrequency, double totalDocumentLength, double collectionLength, double averageDocumentLength)
This method provides the contract for implementing query expansion models. For some models, we have to set the beta and the documentFrequency of a term.- Specified by:
score
in classQueryExpansionModel
- 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.
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