smile.nlp.relevance

Class BM25

java.lang.Object

smile.nlp.relevance.BM25

All Implemented Interfaces:

RelevanceRanker

public class BM25
extends java.lang.Object
implements RelevanceRanker

The BM25 weighting scheme, often called Okapi weighting, after the system in which it was first implemented, was developed as a way of building a probabilistic model sensitive to term frequency and document length while not introducing too many additional parameters into the model. It is not a single function, but actually a whole family of scoring functions, with slightly different components and parameters.

At the extreme values of the coefficient b, BM25 turns into ranking functions known as BM11 (for b = 1) and BM15 (for b = 0). BM25F is a modification of BM25 in which the document is considered to be composed from several fields (such as headlines, main text, anchor text) with possibly different degrees of importance.

BM25 and its newer variants represent state-of-the-art TF-IDF-like retrieval functions used in document retrieval, such as web search.

See Also:

TFIDF

Constructor Summary

Constructors

Constructor and Description
BM25() Default constructor with k1 = 1.2, b = 0.75, delta = 1.0.
BM25(double k1, double b, double delta) Constructor.

Method Summary

Modifier and Type	Method and Description
double	rank(Corpus corpus, TextTerms doc, java.lang.String[] terms, int[] tf, int n) Returns a relevance score between a set of terms and a document based on a corpus.
double	rank(Corpus corpus, TextTerms doc, java.lang.String term, int tf, int n) Returns a relevance score between a term and a document based on a corpus.
double	score(double freq, int docSize, double avgDocSize, long N, long n) Returns a relevance score between a term and a document based on a corpus.
double	score(double freq, long N, long n) Returns a relevance score between a term and a document based on a corpus.
double	score(int termFreq, int docLen, double avgDocLen, int titleTermFreq, int titleLen, double avgTitleLen, int anchorTermFreq, int anchorLen, double avgAnchorLen, long N, long n) Returns a relevance score between a term and a document based on a corpus.

Methods inherited from class java.lang.Object

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

Constructor Detail

BM25

public BM25()

Default constructor with k1 = 1.2, b = 0.75, delta = 1.0.

BM25

public BM25(double k1,
            double b,
            double delta)

Constructor.

Parameters:

k1 - is a positive tuning parameter that calibrates the document term frequency scaling. A k1 value of 0 corresponds to a binary model (no term frequency), and a large value corresponds to using raw term frequency.

b - b is another tuning parameter (0 ≤ b ≤ 1) which determines the scaling by document length: b = 1 corresponds to fully scaling the term weight by the document length, while b = 0 corresponds to no length normalization.

Method Detail

score

public double score(int termFreq,
                    int docLen,
                    double avgDocLen,
                    int titleTermFreq,
                    int titleLen,
                    double avgTitleLen,
                    int anchorTermFreq,
                    int anchorLen,
                    double avgAnchorLen,
                    long N,
                    long n)

Returns a relevance score between a term and a document based on a corpus.

Parameters:

termFreq - normalized term frequency of searching term in the document to rank.

N - the number of documents in the corpus.

n - the number of documents containing the given term in the corpus;

score

public double score(double freq,
                    long N,
                    long n)

Returns a relevance score between a term and a document based on a corpus.

Parameters:

freq - normalized term frequency of searching term in the document to rank.

N - the number of documents in the corpus.

n - the number of documents containing the given term in the corpus;

score

public double score(double freq,
                    int docSize,
                    double avgDocSize,
                    long N,
                    long n)

Returns a relevance score between a term and a document based on a corpus.

Parameters:

freq - the frequency of searching term in the document to rank.

docSize - the size of document to rank.

avgDocSize - the average size of documents in the corpus.

N - the number of documents in the corpus.

n - the number of documents containing the given term in the corpus;

rank

public double rank(Corpus corpus,
                   TextTerms doc,
                   java.lang.String term,
                   int tf,
                   int n)

Description copied from interface: RelevanceRanker

Returns a relevance score between a term and a document based on a corpus.

Specified by:

rank in interface RelevanceRanker

Parameters:

corpus - the corpus.

doc - the document to rank.

term - the searching term.

tf - the term frequency in the document.

n - the number of documents containing the given term in the corpus;

rank

public double rank(Corpus corpus,
                   TextTerms doc,
                   java.lang.String[] terms,
                   int[] tf,
                   int n)

Description copied from interface: RelevanceRanker

Returns a relevance score between a set of terms and a document based on a corpus.

Specified by:

rank in interface RelevanceRanker

Parameters:

corpus - the corpus.

doc - the document to rank.

terms - the searching terms.

tf - the term frequencies in the document.

n - the number of documents containing the given term in the corpus;