public class BM25Similarity extends Similarity
Similarity.SimScorer, Similarity.SimWeight
Constructor and Description |
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BM25Similarity()
BM25 with these default values:
k1 = 1.2 ,
b = 0.75 .
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BM25Similarity(float k1,
float b)
BM25 with the supplied parameter values.
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Modifier and Type | Method and Description |
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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.
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Explanation |
idfExplain(CollectionStatistics collectionStats,
TermStatistics[] termStats)
Computes a score factor for a phrase.
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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. |
String |
toString() |
coord, queryNorm
public BM25Similarity(float k1, float b)
k1
- Controls non-linear term frequency normalization (saturation).b
- Controls to what degree document length normalizes tf values.public BM25Similarity()
k1 = 1.2
,
b = 0.75
.public void setDiscountOverlaps(boolean v)
public boolean getDiscountOverlaps()
setDiscountOverlaps(boolean)
public final long computeNorm(FieldInvertState state)
Similarity
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.
computeNorm
in class Similarity
state
- current processing state for this fieldpublic Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics termStats)
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 computecollectionStats
- collection-level statisticstermStats
- term-level statistics for the termpublic Explanation idfExplain(CollectionStatistics collectionStats, TermStatistics[] termStats)
The default implementation sums the idf factor for each term in the phrase.
collectionStats
- collection-level statisticstermStats
- term-level statistics for the terms in the phrasepublic final Similarity.SimWeight computeWeight(float queryBoost, CollectionStatistics collectionStats, TermStatistics... termStats)
Similarity
computeWeight
in class Similarity
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.public final Similarity.SimScorer simScorer(Similarity.SimWeight stats, AtomicReaderContext context) throws IOException
Similarity
Similarity.SimScorer
to score matching documents from a segment of the inverted index.simScorer
in class Similarity
stats
- collection information from Similarity.computeWeight(float, CollectionStatistics, TermStatistics...)
context
- segment of the inverted index to be scored.context
IOException
- if there is a low-level I/O errorpublic float getK1()
k1
parameterBM25Similarity(float, float)
public float getB()
b
parameterBM25Similarity(float, float)
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