Class LMDirichletSimilarity

Bayesian smoothing using Dirichlet priors. From Chengxiang Zhai and John Lafferty. 2001. A study of smoothing methods for language models applied to Ad Hoc information retrieval. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '01). ACM, New York, NY, USA, 334-342.

The formula as defined the paper assigns a negative score to documents that contain the term, but with fewer occurrences than predicted by the collection language model. The Lucene implementation returns 0 for such documents.

Note

This API is experimental and might change in incompatible ways in the next release.

Inheritance

LMDirichletSimilarity

Inherited Members

LMSimilarity.m_collectionModel

LMSimilarity.NewStats(string, float)

LMSimilarity.ToString()

SimilarityBase.DiscountOverlaps

SimilarityBase.DecodeNormValue(byte)

SimilarityBase.Log2(double)

Similarity.Coord(int, int)

Similarity.QueryNorm(float)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

Namespace: Lucene.Net.Search.Similarities

Assembly: Lucene.Net.dll

Syntax

public class LMDirichletSimilarity : LMSimilarity

Constructors

LMDirichletSimilarity()

Instantiates the similarity with the default μ value of 2000.

Declaration

public LMDirichletSimilarity()

LMDirichletSimilarity(ICollectionModel)

Instantiates the similarity with the default μ value of 2000.

Declaration

public LMDirichletSimilarity(LMSimilarity.ICollectionModel collectionModel)

Parameters

Type	Name	Description
LMSimilarity.ICollectionModel	collectionModel

LMDirichletSimilarity(ICollectionModel, float)

Instantiates the similarity with the provided μ parameter.

Declaration

public LMDirichletSimilarity(LMSimilarity.ICollectionModel collectionModel, float mu)

Parameters

Type	Name	Description
LMSimilarity.ICollectionModel	collectionModel
float	mu

LMDirichletSimilarity(float)

Instantiates the similarity with the provided μ parameter.

Declaration

public LMDirichletSimilarity(float mu)

Parameters

Type	Name	Description
float	mu

Properties

Mu

Returns the μ parameter.

Declaration

public virtual float Mu { get; }

Property Value

Type	Description
float

Methods

Explain(Explanation, BasicStats, int, float, float)

Subclasses should implement this method to explain the score. expl already contains the score, the name of the class and the doc id, as well as the term frequency and its explanation; subclasses can add additional clauses to explain details of their scoring formulae.

The default implementation does nothing.

Declaration

protected override void Explain(Explanation expl, BasicStats stats, int doc, float freq, float docLen)

Parameters

Type	Name	Description
Explanation	expl	the explanation to extend with details.
BasicStats	stats	the corpus level statistics.
int	doc	the document id.
float	freq	the term frequency.
float	docLen	the document length.

Overrides

GetName()

Returns the name of the LM method. The values of the parameters should be included as well.

Used in ToString()

Declaration

public override string GetName()

Returns

Type	Description
string

Overrides

LMSimilarity.GetName()

Score(BasicStats, float, float)

Scores the document doc.

Subclasses must apply their scoring formula in this class.

Declaration

public override float Score(BasicStats stats, float freq, float docLen)

Parameters

Type	Name	Description
BasicStats	stats	the corpus level statistics.
float	freq	the term frequency.
float	docLen	the document length.

Returns

Type	Description
float	the score.