gate.creole.annic.apache.lucene.search
Class Similarity

java.lang.Object
  gate.creole.annic.apache.lucene.search.Similarity

Direct Known Subclasses:

DefaultSimilarity

public abstract class Similarity
extends Object

Expert: Scoring API.

Subclasses implement search scoring.

The score of query q for document d is defined in terms of these methods as follows:

score(q,d) =	Σ	tf(t in d) * idf(t) * getBoost(t.field in d) * lengthNorm(t.field in d)	* coord(q,d) * queryNorm(q)
	t in q

See Also:

setDefault(Similarity), IndexWriter.setSimilarity(Similarity), Searcher.setSimilarity(Similarity)

Constructor Summary

Similarity()

Method Summary

abstract float	coord(int overlap, int maxOverlap) Computes a score factor based on the fraction of all query terms that a document contains.
static float	decodeNorm(byte b) Decodes a normalization factor stored in an index.
static byte	encodeNorm(float f) Encodes a normalization factor for storage in an index.
static Similarity	getDefault() Return the default Similarity implementation used by indexing and search code.
float	idf(Collection terms, Searcher searcher) Computes a score factor for a phrase.
abstract float	idf(int docFreq, int numDocs) Computes a score factor based on a term's document frequency (the number of documents which contain the term).
float	idf(Term term, Searcher searcher) Computes a score factor for a simple term.
abstract float	lengthNorm(String fieldName, int numTokens) Computes the normalization value for a field given the total number of terms contained in a field.
abstract float	queryNorm(float sumOfSquaredWeights) Computes the normalization value for a query given the sum of the squared weights of each of the query terms.
static void	setDefault(Similarity similarity) Set the default Similarity implementation used by indexing and search code.
abstract float	sloppyFreq(int distance) Computes the amount of a sloppy phrase match, based on an edit distance.
abstract float	tf(float freq) Computes a score factor based on a term or phrase's frequency in a document.
float	tf(int freq) Computes a score factor based on a term or phrase's frequency in a document.

Methods inherited from class java.lang.Object

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

Constructor Detail

Similarity

public Similarity()

Method Detail

setDefault

public static void setDefault(Similarity similarity)

Set the default Similarity implementation used by indexing and search code.

See Also:

Searcher.setSimilarity(Similarity), IndexWriter.setSimilarity(Similarity)

getDefault

public static Similarity getDefault()

Return the default Similarity implementation used by indexing and search code.

This is initially an instance of DefaultSimilarity.

See Also:

Searcher.setSimilarity(Similarity), IndexWriter.setSimilarity(Similarity)

decodeNorm

public static float decodeNorm(byte b)

Decodes a normalization factor stored in an index.

See Also:

encodeNorm(float)

lengthNorm

public abstract float lengthNorm(String fieldName,
                                 int numTokens)

Computes the normalization value for a field given the total number of terms contained in a field. These values, together with field boosts, are stored in an index and multipled into scores for hits on each field by the search code.

Matches in longer fields are less precise, so implemenations of this method usually return smaller values when numTokens is large, and larger values when numTokens is small.

That these values are computed under IndexWriter.addDocument(Document) and stored then using {#encodeNorm(float)}. Thus they have limited precision, and documents must be re-indexed if this method is altered.

Parameters:

fieldName - the name of the field

numTokens - the total number of tokens contained in fields named fieldName of doc.

Returns:

a normalization factor for hits on this field of this document

See Also:

Field.setBoost(float)

queryNorm

public abstract float queryNorm(float sumOfSquaredWeights)

Computes the normalization value for a query given the sum of the squared weights of each of the query terms. This value is then multipled into the weight of each query term.

This does not affect ranking, but rather just attempts to make scores from different queries comparable.

Parameters:

sumOfSquaredWeights - the sum of the squares of query term weights

Returns:

a normalization factor for query weights

encodeNorm

public static byte encodeNorm(float f)

Encodes a normalization factor for storage in an index.

The encoding uses a five-bit exponent and three-bit mantissa, thus representing values from around 7x10^9 to 2x10^-9 with about one significant decimal digit of accuracy. Zero is also represented. Negative numbers are rounded up to zero. Values too large to represent are rounded down to the largest representable value. Positive values too small to represent are rounded up to the smallest positive representable value.

See Also:

Field.setBoost(float)

tf

public float tf(int freq)

Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(Term, Searcher) factor for each term in the query and these products are then summed to form the initial score for a document.

Terms and phrases repeated in a document indicate the topic of the document, so implementations of this method usually return larger values when freq is large, and smaller values when freq is small.

The default implementation calls tf(float).

Parameters:

freq - the frequency of a term within a document

Returns:

a score factor based on a term's within-document frequency

sloppyFreq

public abstract float sloppyFreq(int distance)

Computes the amount of a sloppy phrase match, based on an edit distance. This value is summed for each sloppy phrase match in a document to form the frequency that is passed to tf(float).

A phrase match with a small edit distance to a document passage more closely matches the document, so implementations of this method usually return larger values when the edit distance is small and smaller values when it is large.

Parameters:

distance - the edit distance of this sloppy phrase match

Returns:

the frequency increment for this match

See Also:

PhraseQuery.setSlop(int)

tf

public abstract float tf(float freq)

Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the idf(Term, Searcher) factor for each term in the query and these products are then summed to form the initial score for a document.

Terms and phrases repeated in a document indicate the topic of the document, so implemenations of this method usually return larger values when freq is large, and smaller values when freq is small.

Parameters:

freq - the frequency of a term within a document

Returns:

a score factor based on a term's within-document frequency

idf

public float idf(Term term,
                 Searcher searcher)
          throws IOException

Computes a score factor for a simple term.

The default implementation is:

   return idf(searcher.docFreq(term), searcher.maxDoc());
 

Note that Searchable.maxDoc() is used instead of IndexReader.numDocs() because it is proportional to Searchable.docFreq(Term) , i.e., when one is inaccurate, so is the other, and in the same direction.

Parameters:

term - the term in question

searcher - the document collection being searched

Returns:

a score factor for the term

Throws:

IOException

idf

public float idf(Collection terms,
                 Searcher searcher)
          throws IOException

Computes a score factor for a phrase.

The default implementation sums the idf(Term,Searcher) factor for each term in the phrase.

Parameters:

terms - the terms in the phrase

searcher - the document collection being searched

Returns:

a score factor for the phrase

Throws:

IOException

idf

public abstract float idf(int docFreq,
                          int numDocs)

Computes a score factor based on a term's document frequency (the number of documents which contain the term). This value is multiplied by the tf(int) factor for each term in the query and these products are then summed to form the initial score for a document.

Terms that occur in fewer documents are better indicators of topic, so implemenations of this method usually return larger values for rare terms, and smaller values for common terms.

Parameters:

docFreq - the number of documents which contain the term

numDocs - the total number of documents in the collection

Returns:

a score factor based on the term's document frequency

coord

public abstract float coord(int overlap,
                            int maxOverlap)

Computes a score factor based on the fraction of all query terms that a document contains. This value is multiplied into scores.

The presence of a large portion of the query terms indicates a better match with the query, so implemenations of this method usually return larger values when the ratio between these parameters is large and smaller values when the ratio between them is small.

Parameters:

overlap - the number of query terms matched in the document

maxOverlap - the total number of terms in the query

Returns:

a score factor based on term overlap with the query