| AfterEffect |
This class acts as the base class for the implementations of the first
normalization of the informative content in the DFR framework.
|
| AfterEffectB |
Model of the information gain based on the ratio of two Bernoulli processes.
|
| AfterEffectL |
Model of the information gain based on Laplace's law of succession.
|
| BasicModel |
This class acts as the base class for the specific basic model
implementations in the DFR framework.
|
| BasicModelG |
Geometric as limiting form of the Bose-Einstein model.
|
| BasicModelIF |
An approximation of the I(ne) model.
|
| BasicModelIn |
The basic tf-idf model of randomness.
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| BasicModelIne |
Tf-idf model of randomness, based on a mixture of Poisson and inverse
document frequency.
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| BasicStats |
Stores all statistics commonly used ranking methods.
|
| BM25Similarity |
BM25 Similarity.
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| BooleanSimilarity |
Simple similarity that gives terms a score that is equal to their query
boost.
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| ClassicSimilarity |
Expert: Historical scoring implementation.
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| DFISimilarity |
Implements the Divergence from Independence (DFI) model based on Chi-square statistics
(i.e., standardized Chi-squared distance from independence in term frequency tf).
|
| DFRSimilarity |
Implements the divergence from randomness (DFR) framework
introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.
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| Distribution |
The probabilistic distribution used to model term occurrence
in information-based models.
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| DistributionLL |
Log-logistic distribution.
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| DistributionSPL |
The smoothed power-law (SPL) distribution for the information-based framework
that is described in the original paper.
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| IBSimilarity |
Provides a framework for the family of information-based models, as described
in Stéphane Clinchant and Eric Gaussier.
|
| Independence |
Computes the measure of divergence from independence for DFI
scoring functions.
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| IndependenceChiSquared |
Normalized chi-squared measure of distance from independence
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| IndependenceSaturated |
Saturated measure of distance from independence
|
| IndependenceStandardized |
Standardized measure of distance from independence
|
| Lambda |
The lambda (λw) parameter in information-based
models.
|
| LambdaDF |
Computes lambda as docFreq+1 / numberOfDocuments+1.
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| LambdaTTF |
Computes lambda as totalTermFreq+1 / numberOfDocuments+1.
|
| LMDirichletSimilarity |
Bayesian smoothing using Dirichlet priors.
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| LMJelinekMercerSimilarity |
Language model based on the Jelinek-Mercer smoothing method.
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| LMSimilarity |
Abstract superclass for language modeling Similarities.
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| LMSimilarity.DefaultCollectionModel |
Models p(w|C) as the number of occurrences of the term in the
collection, divided by the total number of tokens + 1.
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| LMSimilarity.LMStats |
Stores the collection distribution of the current term.
|
| MultiSimilarity |
Implements the CombSUM method for combining evidence from multiple
similarity values described in: Joseph A.
|
| Normalization |
This class acts as the base class for the implementations of the term
frequency normalization methods in the DFR framework.
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| Normalization.NoNormalization |
Implementation used when there is no normalization.
|
| NormalizationH1 |
Normalization model that assumes a uniform distribution of the term frequency.
|
| NormalizationH2 |
Normalization model in which the term frequency is inversely related to the
length.
|
| NormalizationH3 |
Dirichlet Priors normalization
|
| NormalizationZ |
Pareto-Zipf Normalization
|
| PerFieldSimilarityWrapper |
Provides the ability to use a different Similarity for different fields. |
| Similarity |
Similarity defines the components of Lucene scoring.
|
| Similarity.SimScorer |
Stores the weight for a query across the indexed collection.
|
| SimilarityBase |
A subclass of Similarity that provides a simplified API for its
descendants.
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| TFIDFSimilarity |
Implementation of Similarity with the Vector Space Model. |