org.terrier.matching.models

Class Hiemstra_LM

java.lang.Object

org.terrier.matching.models.WeightingModel

org.terrier.matching.models.Hiemstra_LM

All Implemented Interfaces:

Serializable, Cloneable, Model

public class Hiemstra_LM
extends WeightingModel

This class implements the Hiemstra LM weighting model. A default lambda value of 0.15 is used, according to section 5.2.3 of Djoerd Hiemstra's thesis: Using language models for information retrieval. PhD Thesis, Centre for Telematics and Information Technology, University of Twente, 2001.

Author:

Jie Peng

See Also:

Serialized Form

Field Summary

Fields inherited from class org.terrier.matching.models.WeightingModel

averageDocumentLength, c, cs, documentFrequency, es, i, keyFrequency, numberOfDocuments, numberOfPointers, numberOfTokens, numberOfUniqueTerms, rq, termFrequency

Constructor Summary

Constructors

Constructor and Description
Hiemstra_LM() A default constructor.
Hiemstra_LM(double lambda) Constructs an instance of this class with the specified value for the parameter lambda.

Method Summary

Methods

Modifier and Type	Method and Description
String	getInfo() Returns the name of the model.
double	score(double tf, double docLength) Uses Hiemestra_LM to compute a weight for a term in a document.
double	score(double tf, double docLength, double n_t, double F_t, double keyFrequency) Deprecated.

Methods inherited from class org.terrier.matching.models.WeightingModel

clone, getOverflowed, getParameter, prepare, score, setCollectionStatistics, setEntryStatistics, setKeyFrequency, setParameter, setRequest

Methods inherited from class java.lang.Object

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

Constructor Detail

Hiemstra_LM

public Hiemstra_LM()

A default constructor. Uses the default value of lambda=0.15.

Hiemstra_LM

public Hiemstra_LM(double lambda)

Constructs an instance of this class with the specified value for the parameter lambda.

Parameters:

lambda - the smoothing parameter.

Method Detail

getInfo

public final String getInfo()

Returns the name of the model.

Specified by:

getInfo in interface Model

Specified by:

getInfo in class WeightingModel

Returns:

the name of the model

score

public final double score(double tf,
           double docLength)

Uses Hiemestra_LM to compute a weight for a term in a document.

Specified by:

score in class WeightingModel

Parameters:

tf - The term frequency in the document

docLength - the document's length

Returns:

the score assigned to a document with the given tf and docLength, and other preset parameters

score

@Deprecated
public final double score(double tf,
                      double docLength,
                      double n_t,
                      double F_t,
                      double keyFrequency)

Deprecated.

This method provides the contract for implementing weighting models. As of Terrier 3.6, the 5-parameter score method is being deprecated since it is not used. The two parameter score method should be used instead. Tagged for removal in a later version.

Specified by:

score in class WeightingModel

Parameters:

tf - The term frequency in the document

docLength - the document's length

n_t - The document frequency of the term

F_t - the term frequency in the collection

keyFrequency - the term frequency in the query

Returns:

the score returned by the implemented weighting model.