Class LMJelinekMercerSimilarity

Language model based on the Jelinek-Mercer smoothing method. 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 model has a single parameter, λ. According to said paper, the optimal value depends on both the collection and the query. The optimal value is around 0.1 for title queries and 0.7 for long queries.

Note

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

Inheritance

LMJelinekMercerSimilarity

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 LMJelinekMercerSimilarity : LMSimilarity

Constructors

LMJelinekMercerSimilarity(ICollectionModel, float)

Instantiates with the specified collectionModel and λ parameter.

Declaration

public LMJelinekMercerSimilarity(LMSimilarity.ICollectionModel collectionModel, float lambda)

Parameters

Type	Name	Description
LMSimilarity.ICollectionModel	collectionModel
float	lambda

LMJelinekMercerSimilarity(float)

Instantiates with the specified λ parameter.

Declaration

public LMJelinekMercerSimilarity(float lambda)

Parameters

Type	Name	Description
float	lambda

Properties

Lambda

Returns the λ parameter.

Declaration

public virtual float Lambda { 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.