org.terrier.matching.models.queryexpansion
Class Information

java.lang.Object
  extended by org.terrier.matching.models.queryexpansion.QueryExpansionModel
      extended by org.terrier.matching.models.queryexpansion.Information

public class Information
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
Information()
          A default constructor.
 
Method Summary
 java.lang.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

Information

public Information()
A default constructor.

Method Detail

getInfo

public final java.lang.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.


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