Package org.apache.lucene.search.similarities

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.

Nested Class Summary

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

Similarity.SimScorer, Similarity.SimWeight

Constructor Summary

Constructors

Constructor	Description
BM25Similarity()	BM25 with these default values: k1 = 1.2, b = 0.75.
BM25Similarity(float k1, float b)	BM25 with the supplied parameter values.

Method Summary

Modifier and Type	Method	Description
long	computeNorm(FieldInvertState state)	Computes the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState).
Similarity.SimWeight	computeWeight(float queryBoost, CollectionStatistics collectionStats, TermStatistics... termStats)	Compute any collection-level weight (e.g.
float	getB()	Returns the b parameter
boolean	getDiscountOverlaps()	Returns true if overlap tokens are discounted from the document's length.
float	getK1()	Returns the k1 parameter
Explanation	idfExplain(CollectionStatistics collectionStats, TermStatistics termStats)	Computes a score factor for a simple term and returns an explanation for that score factor.
Explanation	idfExplain(CollectionStatistics collectionStats, TermStatistics[] termStats)	Computes a score factor for a phrase.
void	setDiscountOverlaps(boolean v)	Sets whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm.
Similarity.SimScorer	simScorer(Similarity.SimWeight stats, AtomicReaderContext context)	Creates a new Similarity.SimScorer to score matching documents from a segment of the inverted index.
java.lang.String	toString()

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

coord, queryNorm

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

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.

BM25Similarity

public BM25Similarity()

BM25 with these default values:

• k1 = 1.2,
• b = 0.75.

Method Detail

setDiscountOverlaps

public void setDiscountOverlaps(boolean v)

Sets whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm. By default this is true, meaning overlap tokens do not count when computing norms.

getDiscountOverlaps

public boolean getDiscountOverlaps()

Returns true if overlap tokens are discounted from the document's length.

See Also:

setDiscountOverlaps(boolean)

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.

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, searcher.maxDoc());
 

Note that CollectionStatistics.maxDoc() is used instead of IndexReader#numDocs() because also TermStatistics.docFreq() is used, and when the latter is inaccurate, so is CollectionStatistics.maxDoc(), and in the same direction. In addition, CollectionStatistics.maxDoc() is more efficient to compute

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.

computeWeight

public final Similarity.SimWeight computeWeight(float queryBoost,
                                                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:

computeWeight in class Similarity

Parameters:

queryBoost - the query-time boost.

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.

simScorer

public final Similarity.SimScorer simScorer(Similarity.SimWeight stats,
                                            AtomicReaderContext context)
                                     throws java.io.IOException

Description copied from class: Similarity

Creates a new Similarity.SimScorer to score matching documents from a segment of the inverted index.

Specified by:

simScorer in class Similarity

Parameters:

stats - collection information from Similarity.computeWeight(float, CollectionStatistics, TermStatistics...)

context - segment of the inverted index to be scored.

Returns:

SloppySimScorer for scoring documents across context

Throws:

java.io.IOException - if there is a low-level I/O error

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getK1

public float getK1()

Returns the k1 parameter

See Also:

BM25Similarity(float, float)

getB

public float getB()

Returns the b parameter

See Also:

BM25Similarity(float, float)