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java.lang.Object org.terrier.matching.models.WeightingModel org.terrier.matching.models.Hiemstra_LM
public class Hiemstra_LM
This class implements the Hiemstra LM weighting model. A default lambda value of 0.15 is used, according to section 5.2.3 of Djoerd Hiemstra's thesis: Using language models for information retrieval. PhD Thesis, Centre for Telematics and Information Technology, University of Twente, 2001.
Field Summary |
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Fields inherited from class org.terrier.matching.models.WeightingModel |
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averageDocumentLength, c, documentFrequency, i, keyFrequency, numberOfDocuments, numberOfPointers, numberOfTokens, numberOfUniqueTerms, termFrequency |
Constructor Summary | |
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Hiemstra_LM()
A default constructor. |
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Hiemstra_LM(double lambda)
Constructs an instance of this class with the specified value for the parameter lambda. |
Method Summary | |
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java.lang.String |
getInfo()
Returns the name of the model. |
double |
score(double tf,
double docLength)
Uses Hiemestra_LM to compute a weight for a term in a document. |
double |
score(double tf,
double docLength,
double n_t,
double F_t,
double keyFrequency)
Uses Hiemstra_LM to compute a weight for a term in a document. |
Methods inherited from class org.terrier.matching.models.WeightingModel |
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clone, getOverflowed, getParameter, prepare, score, setAverageDocumentLength, setCollectionStatistics, setDocumentFrequency, setEntryStatistics, setKeyFrequency, setNumberOfDocuments, setNumberOfPointers, setNumberOfTokens, setNumberOfUniqueTerms, setParameter, setRequest, setTermFrequency, stirlingPower |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public Hiemstra_LM()
public Hiemstra_LM(double lambda)
lambda
- the smoothing parameter.Method Detail |
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public final java.lang.String getInfo()
getInfo
in interface Model
getInfo
in class WeightingModel
public final double score(double tf, double docLength)
score
in class WeightingModel
tf
- The term frequency in the documentdocLength
- the document's length
public final double score(double tf, double docLength, double n_t, double F_t, double keyFrequency)
score
in class WeightingModel
tf
- The term frequency in the documentdocLength
- the document's lengthn_t
- The document frequency of the termF_t
- the term frequency in the collectionkeyFrequency
- the term frequency in the query
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