Package org.apache.lucene.search.similarities

Class DFRSimilarity

java.lang.Object
org.apache.lucene.search.similarities.Similarity
org.apache.lucene.search.similarities.SimilarityBase
org.apache.lucene.search.similarities.DFRSimilarity
public class DFRSimilarity extends SimilarityBase
Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen. 2002. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20, 4 (October 2002), 357-389.

The DFR scoring formula is composed of three separate components: the basic model, the aftereffect and an additional normalization component, represented by the classes BasicModel, AfterEffect and Normalization, respectively. The names of these classes were chosen to match the names of their counterparts in the Terrier IR engine.

To construct a DFRSimilarity, you must specify the implementations for all three components of DFR:

  1. BasicModel: Basic model of information content:
  2. AfterEffect: First normalization of information gain:
  3. Normalization: Second (length) normalization:

Note that qtf, the multiplicity of term-occurrence in the query, is not handled by this implementation.

Note that basic models BE (Limiting form of Bose-Einstein), P (Poisson approximation of the Binomial) and D (Divergence approximation of the Binomial) are not implemented because their formula couldn't be written in a way that makes scores non-decreasing with the normalized term frequency.

See Also:
WARNING: This API is experimental and might change in incompatible ways in the next release.

  • Field Details

    • basicModel

      protected final BasicModel basicModel
      The basic model for information content.

    • afterEffect

      protected final AfterEffect afterEffect
      The first normalization of the information content.

    • normalization

      protected final Normalization normalization
      The term frequency normalization.

  • Constructor Details

    • DFRSimilarity

      public DFRSimilarity(BasicModel basicModel, AfterEffect afterEffect, Normalization normalization)
      Creates DFRSimilarity from the three components.

      Note that null values are not allowed: if you want no normalization, instead pass Normalization.NoNormalization.

      Parameters:
      basicModel - Basic model of information content
      afterEffect - First normalization of information gain
      normalization - Second (length) normalization

  • Method Details

    • score

      protected double score(BasicStats stats, double freq, double docLen)
      Description copied from class: SimilarityBase
      Scores the document doc.

      Subclasses must apply their scoring formula in this class.

      Specified by:
      score in class SimilarityBase
      Parameters:
      stats - the corpus level statistics.
      freq - the term frequency.
      docLen - the document length.
      Returns:
      the score.

    • explain

      protected void explain(List<Explanation> subs, BasicStats stats, double freq, double docLen)
      Description copied from class: SimilarityBase
      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.

      Overrides:
      explain in class SimilarityBase
      Parameters:
      subs - the list of details of the explanation to extend
      stats - the corpus level statistics.
      freq - the term frequency.
      docLen - the document length.

    • explain

      protected Explanation explain(BasicStats stats, Explanation freq, double docLen)
      Description copied from class: SimilarityBase
      Explains the score. The implementation here provides a basic explanation in the format score(name-of-similarity, doc=doc-id, freq=term-frequency), computed from:, and attaches the score (computed via the SimilarityBase.score(BasicStats, double, double) method) and the explanation for the term frequency. Subclasses content with this format may add additional details in SimilarityBase.explain(List, BasicStats, double, double).
      Overrides:
      explain in class SimilarityBase
      Parameters:
      stats - the corpus level statistics.
      freq - the term frequency and its explanation.
      docLen - the document length.
      Returns:
      the explanation.

    • toString

      public String toString()
      Description copied from class: SimilarityBase
      Subclasses must override this method to return the name of the Similarity and preferably the values of parameters (if any) as well.
      Specified by:
      toString in class SimilarityBase

    • getBasicModel

      public BasicModel getBasicModel()
      Returns the basic model of information content

    • getAfterEffect

      public AfterEffect getAfterEffect()
      Returns the first normalization

    • getNormalization

      public Normalization getNormalization()
      Returns the second normalization