Class BM25Similarity

java.lang.Object
org.apache.lucene.search.similarities.Similarity
org.apache.lucene.search.similarities.BM25Similarity

public class BM25Similarity extends Similarity
BM25 Similarity. Introduced in Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. Okapi at TREC-3. In Proceedings of the Third Text REtrieval Conference (TREC 1994). Gaithersburg, USA, November 1994.
  • Constructor Details

    • BM25Similarity

      public BM25Similarity(float k1, float b, boolean discountOverlaps)
      BM25 with the supplied parameter values.
      Parameters:
      k1 - Controls non-linear term frequency normalization (saturation).
      b - Controls to what degree document length normalizes tf values.
      discountOverlaps - True if overlap tokens (tokens with a position of increment of zero) are discounted from the document's length.
      Throws:
      IllegalArgumentException - if k1 is infinite or negative, or if b is not within the range [0..1]
    • BM25Similarity

      public BM25Similarity(float k1, float b)
      BM25 with the supplied parameter values.
      Parameters:
      k1 - Controls non-linear term frequency normalization (saturation).
      b - Controls to what degree document length normalizes tf values.
      Throws:
      IllegalArgumentException - if k1 is infinite or negative, or if b is not within the range [0..1]
    • BM25Similarity

      public BM25Similarity(boolean discountOverlaps)
      BM25 with these default values:
      • k1 = 1.2
      • b = 0.75
      and the supplied parameter value:
      Parameters:
      discountOverlaps - True if overlap tokens (tokens with a position of increment of zero) are discounted from the document's length.
    • BM25Similarity

      public BM25Similarity()
      BM25 with these default values:
      • k1 = 1.2
      • b = 0.75
      • discountOverlaps = true
  • Method Details

    • idf

      protected float idf(long docFreq, long docCount)
      Implemented as log(1 + (docCount - docFreq + 0.5)/(docFreq + 0.5)).
    • avgFieldLength

      protected float avgFieldLength(CollectionStatistics collectionStats)
      The default implementation computes the average as sumTotalTermFreq / docCount
    • getDiscountOverlaps

      public boolean getDiscountOverlaps()
      Returns true if overlap tokens are discounted from the document's length.
      See Also:
    • computeNorm

      public final long computeNorm(FieldInvertState state)
      Description copied from class: Similarity
      Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).

      Matches in longer fields are less precise, so implementations of this method usually set smaller values when state.getLength() is large, and larger values when state.getLength() is small.

      Note that for a given term-document frequency, greater unsigned norms must produce scores that are lower or equal, ie. for two encoded norms n1 and n2 so that Long.compareUnsigned(n1, n2) > 0 then SimScorer.score(freq, n1) <= SimScorer.score(freq, n2) for any legal freq.

      0 is not a legal norm, so 1 is the norm that produces the highest scores.

      Specified by:
      computeNorm in class Similarity
      Parameters:
      state - current processing state for this field
      Returns:
      computed norm value
    • idfExplain

      public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats)
      Computes a score factor for a simple term and returns an explanation for that score factor.

      The default implementation uses:

       idf(docFreq, docCount);
       
      Note that CollectionStatistics.docCount() is used instead of IndexReader#numDocs() because also TermStatistics.docFreq() is used, and when the latter is inaccurate, so is CollectionStatistics.docCount(), and in the same direction. In addition, CollectionStatistics.docCount() does not skew when fields are sparse.
      Parameters:
      collectionStats - collection-level statistics
      termStats - term-level statistics for the term
      Returns:
      an Explain object that includes both an idf score factor and an explanation for the term.
    • idfExplain

      public Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics[] termStats)
      Computes a score factor for a phrase.

      The default implementation sums the idf factor for each term in the phrase.

      Parameters:
      collectionStats - collection-level statistics
      termStats - term-level statistics for the terms in the phrase
      Returns:
      an Explain object that includes both an idf score factor for the phrase and an explanation for each term.
    • scorer

      public final Similarity.SimScorer scorer(float boost, CollectionStatistics collectionStats, TermStatistics... termStats)
      Description copied from class: Similarity
      Compute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.
      Specified by:
      scorer in class Similarity
      Parameters:
      boost - a multiplicative factor to apply to the produces scores
      collectionStats - collection-level statistics, such as the number of tokens in the collection.
      termStats - term-level statistics, such as the document frequency of a term across the collection.
      Returns:
      SimWeight object with the information this Similarity needs to score a query.
    • toString

      public String toString()
      Overrides:
      toString in class Object
    • getK1

      public final float getK1()
      Returns the k1 parameter
      See Also:
    • getB

      public final float getB()
      Returns the b parameter
      See Also: