Package org.apache.lucene.search.similarities

# Class LMDirichletSimilarity

java.lang.Object
org.apache.lucene.search.similarities.Similarity
org.apache.lucene.search.similarities.SimilarityBase
org.apache.lucene.search.similarities.LMSimilarity
org.apache.lucene.search.similarities.LMDirichletSimilarity

public class LMDirichletSimilarity extends LMSimilarity
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.

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

- ## Nested Class Summary

  ## Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.LMSimilarity

  `LMSimilarity.CollectionModel, LMSimilarity.DefaultCollectionModel, LMSimilarity.LMStats`

  ## Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity

  `Similarity.BulkSimScorer, Similarity.SimScorer`

- ## Field Summary

  ### Fields inherited from class org.apache.lucene.search.similarities.LMSimilarity

  `collectionModel`

- ## Constructor Summary

  Constructors
  Constructor
  Description
  `LMDirichletSimilarity()`
  Instantiates the similarity with the default μ value of 2000.
  `LMDirichletSimilarity(float mu)`
  Instantiates the similarity with the provided μ parameter.
  `LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel)`
  Instantiates the similarity with the default μ value of 2000.
  `LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel, boolean discountOverlaps, float mu)`
  Instantiates the similarity with the provided parameters.
  `LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel, float mu)`
  Instantiates the similarity with the provided μ parameter.

- ## Method Summary

  Modifier and Type
  Method
  Description
  `protected void`
  `explain(List<Explanation> subs, BasicStats stats, double freq, double docLen)`
  Subclasses should implement this method to explain the score.
  `protected Explanation`
  `explain(BasicStats stats, Explanation freq, double docLen)`
  Explains the score.
  `float`
  `getMu()`
  Returns the μ parameter.
  `String`
  `getName()`
  Returns the name of the LM method.
  `protected double`
  `score(BasicStats stats, double freq, double docLen)`
  Scores the document `doc`.

  ### Methods inherited from class org.apache.lucene.search.similarities.LMSimilarity

  `fillBasicStats, newStats, toString`

  ### Methods inherited from class org.apache.lucene.search.similarities.SimilarityBase

  `log2, scorer`

  ### Methods inherited from class org.apache.lucene.search.similarities.Similarity

  `computeNorm, getDiscountOverlaps`

  ### Methods inherited from class java.lang.Object

  `clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- ## Constructor Details

  - ### LMDirichletSimilarity

    public LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel, float mu)
    Instantiates the similarity with the provided μ parameter.

  - ### LMDirichletSimilarity

    public LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel, boolean discountOverlaps, float mu)
    Instantiates the similarity with the provided parameters.

  - ### LMDirichletSimilarity

    public LMDirichletSimilarity(float mu)
    Instantiates the similarity with the provided μ parameter.

  - ### LMDirichletSimilarity

    public LMDirichletSimilarity(LMSimilarity.CollectionModel collectionModel)
    Instantiates the similarity with the default μ value of 2000.

  - ### LMDirichletSimilarity

    public LMDirichletSimilarity()
    Instantiates the similarity with the default μ value of 2000.

- ## 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 `LMSimilarity`
    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.

  - ### getMu

    public float getMu()
    Returns the μ parameter.

  - ### getName

    public String getName()
    Description copied from class: `LMSimilarity`
    Returns the name of the LM method. The values of the parameters should be included as well.

    Used in `LMSimilarity.toString()`.

    Specified by:
      `getName` in class `LMSimilarity`