org.terrier.matching.models
Class DirichletLM

java.lang.Object
  org.terrier.matching.models.WeightingModel
      org.terrier.matching.models.DirichletLM

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Model

public class DirichletLM
extends WeightingModel

Bayesian smoothing with Dirichlet Prior. This has one parameter, mu > 0. "The optimal value of mu also tends to be larger for long queries than for title queries. The optimal ... seems to vary from collection to collection, though in most cases, it is around 2,000. The tail of the curves is generally flat." This class sets mu to 2500 by default. As a default, this gives higher performance than BM25 (b=0.75) on TREC Terabyte track 2004.

The retrieval performance of this weighting model has been empirically verified to be similar to that reported below. This model is formulated such that all scores are > 0.

A Study of Smoothing Methods for Language Models Applied to Information Retrieval. Zhai & Lafferty, ACM Transactions on Information Systems, Vol. 22, No. 2, April 2004, Pages 179--214.

Since:

3.0

Author:

Craig Macdonald

See Also:

Serialized Form

Field Summary

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

averageDocumentLength, c, documentFrequency, i, keyFrequency, numberOfDocuments, numberOfPointers, numberOfTokens, numberOfUniqueTerms, termFrequency

Constructor Summary

DirichletLM()
Constructs an instance of DirichletLM

Method Summary

java.lang.String	getInfo() Returns the name of the model.
double	score(double tf, double docLength) This method provides the contract for implementing weighting models.
double	score(double tf, double docLength, double n_t, double F_t, double keyFrequency) This method provides the contract for implementing weighting models.

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

clone, getOverflowed, getParameter, prepare, score, setAverageDocumentLength, setCollectionStatistics, setDocumentFrequency, setEntryStatistics, setKeyFrequency, setNumberOfDocuments, setNumberOfPointers, setNumberOfTokens, setNumberOfUniqueTerms, setParameter, setRequest, setTermFrequency, stirlingPower

Methods inherited from class java.lang.Object

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

Constructor Detail

DirichletLM

public DirichletLM()

Constructs an instance of DirichletLM

Method Detail

getInfo

public java.lang.String getInfo()

Description copied from class: WeightingModel

Returns the name of the model.

Specified by:

getInfo in interface Model

Specified by:

getInfo in class WeightingModel

Returns:

java.lang.String

score

public double score(double tf,
                    double docLength)

Description copied from class: WeightingModel

This method provides the contract for implementing weighting models.

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

public double score(double tf,
                    double docLength,
                    double n_t,
                    double F_t,
                    double keyFrequency)

Description copied from class: WeightingModel

This method provides the contract for implementing weighting models.

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.