Package  Description 

org.terrier.matching 
Provides the classes and interfaces used for matching documents
to queries.

org.terrier.matching.daat 
Provides classes that implement a documentatatime (DAAT) matching strategy.

org.terrier.matching.models 
Provides the classes that implement various weighting models.

org.terrier.matching.taat 
Provides classes that implement a termatatime (TAAT) matching strategy.

org.terrier.matching.tsms 
Provides the interface and classes that implement the term
score modifiers, which modify the scores assigned to documents
for a particular term.

Modifier and Type  Field and Description 

protected WeightingModel 
MatchingQueryTerms.defaultWeightingModel
default weighting model for all terms

protected WeightingModel[][] 
BaseMatching.wm 
Modifier and Type  Method and Description 

WeightingModel[] 
MatchingQueryTerms.getTermWeightingModels(String term)
Returns the weighting models to be used for a given term.

Modifier and Type  Method and Description 

void 
PostingListManager.addSingleTerm(String queryTerm,
double weight,
EntryStatistics entryStats,
WeightingModel[] wmodels)
Add a single term to those to be matched for this query.

void 
PostingListManager.addSingleTermAlternatives(String[] terms,
String stringForm,
double weight,
EntryStatistics[] entryStats,
WeightingModel[] wmodels)
Adds a synonym group to the matching process.

void 
PostingListManager.addSingleTermAlternatives(String[] terms,
String stringForm,
double weight,
EntryStatistics entryStats,
WeightingModel[] wmodels)
Adds a synonym group to the matching process.

void 
MatchingQueryTerms.setDefaultTermWeightingModel(WeightingModel weightingModel)
Set the default weighting model to be used for all terms

void 
MatchingQueryTerms.setTermProperty(String term,
double weight,
WeightingModel tsm)
Sets the weight and a term score modifier for the given query term.

void 
MatchingQueryTerms.setTermProperty(String term,
WeightingModel tsm)
Sets a term score modifier for the given query term.

Constructor and Description 

FatScoringMatching(Index _index,
Matching _parent,
WeightingModel _wm) 
MatchingQueryTerms.QueryTermProperties(int _index,
double w,
WeightingModel model)
A constructor for setting the weight and a
term score modifier for a term.

MatchingQueryTerms.QueryTermProperties(int _index,
double w,
WeightingModel model,
EntryStatistics _stats)
A constructor for setting a weight, a term score modifier
and the term code for a query term.

MatchingQueryTerms.QueryTermProperties(int _index,
WeightingModel model)
A constructor for setting a term score modifier for a term.

MatchingQueryTerms.QueryTermProperties(int _index,
WeightingModel model,
EntryStatistics _stats)
A constructor for setting a term score modifier for a term
and its term code.

Modifier and Type  Method and Description 

protected double 
FullNoPLM.scoreIt(WeightingModel[] wModels,
Posting posting)
calculate the score for this posting using the specified weighting models

Modifier and Type  Class and Description 

class 
BB2
This class implements the BB2 weighting model.

class 
BM25
This class implements the Okapi BM25 weighting model.

class 
BM25F
A convenience subclass of PerFieldNormWeightingModel setup to
do specifically BM25F, as described by [Zaragoza TREC2004].

class 
DFIC
Divergence From Independence model based on Chisquare statistics
(i.e., standardized Chisquared distance from independence in term frequency tf).

class 
DFIZ
Divergence From Independence model based on Standardization
(i.e., standardized distance from independence in term frequency tf).

class 
DFR_BM25
This class implements the DFR_BM25 weighting model.

class 
DFRee
This class implements the DFRee weighting model.

class 
DFReeKLIM
This class implements the DFReeKLIM weighting model.

class 
DFRWeightingModel
This class implements a modular Divergence from Randomness weighting model.

class 
DirichletLM
Bayesian smoothing with Dirichlet Prior.

class 
Dl
This class implements a simple document length weighting model.

class 
DLH
This class implements the DLH weighting model.

class 
DLH13
This class implements the DLH13 weighting model.

class 
DPH
This class implements the DPH hypergeometric weighting model.

class 
Hiemstra_LM
This class implements the Hiemstra LM weighting model.

class 
IFB2
This class implements the IFB2 weighting model.

class 
In_expB2
This class implements the In_expB2 weighting model, namely Inverse Expected Document Frequency model with
Bernoulli aftereffect and normalisation 2.

class 
In_expC2
This class implements the In_expC2 weighting model.

class 
InB2
This class implements the InB2 weighting model, namely
Inverse Document Frequency model with Bernoulli aftereffect and normalisation 2.

class 
InL2
This class implements the InL2 weighting model.

class 
Js_KLs
This class implements the Js_KLs weighting model, which is the product
of two measures: the Jefrreys' divergence with the Kullback Leibler's divergence.

class 
LemurTF_IDF
This class implements the TF_IDF weighting model as it is implemented in Lemur.

class 
LGD
This class implements the LGD weighting model.

class 
MDL2
This class implements the MDL2 fieldbased weighting model.

class 
ML2
This class implements the ML2 fieldbased weighting model.

class 
PerFieldNormWeightingModel
A class for generating arbitrary perfield normalisation models.

class 
PL2
This class implements the PL2 weighting model.

class 
PL2F
A convenience subclass of PerFieldNormWeightingModel setup to
do specifically PL2F.

class 
SingleFieldModel
Use a normal weighting model on a predetermine subset of the field.

class 
StaticFeature
Class for query independent features loaded from file.

class 
StaticScoreModifierWeightingModel
Base abstract class for query independent features loaded from file.

class 
Tf
This class implements a simple Tf weighting model.

class 
TF_IDF
This class implements the TF_IDF weighting model.

class 
XSqrA_M
This class implements the XSqrA_M weighting model, which computed the
inner product of Pearson's X^2 with the information growth computed
with the multinomial M.

Modifier and Type  Method and Description 

WeightingModel 
WeightingModel.clone()
Clone this weighting model

static WeightingModel 
WeightingModelFactory.newInstance(String name)
Returns the requested weighting model.

static WeightingModel 
WeightingModelFactory.newInstance(String name,
Index index)
Returns the requested weighting model for the specified index.

Modifier and Type  Method and Description 

protected void 
FullNoPLM.assignScores(int i,
WeightingModel[] wModels,
AccumulatorResultSet rs,
IterablePosting postings) 
Modifier and Type  Class and Description 

class 
RequiredTermModifier
Resets the scores of documents according to whether a term is required
or not, and whether it appears in the retrieved documents.

class 
TermInFieldModifier
Resets the scores of documents according to whether a term appears in
a given set of fields.

Terrier Information Retrieval Platform4.1. Copyright © 20042015, University of Glasgow