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Packages that use FeatureVector | |
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de.fu_berlin.ties.classify | This package provides functionality for classification of texts and feature vectors. |
de.fu_berlin.ties.classify.feature | This package contains classes for working with features, feature vectors and feature transformers. |
de.fu_berlin.ties.classify.winnow | This package contains the Winnow classification algorithm and related algorithms and classes. |
de.fu_berlin.ties.context | This packages provides functionality for building and managing representations of context in texts (XML documents). |
de.fu_berlin.ties.eval | This packages provides functionality for evaluating results of classifiers and extractors. |
de.fu_berlin.ties.extract | This package handles information extraction and entitiy recognition. |
de.fu_berlin.ties.extract.amend | This package provides code for reanalysing proposed extractions and performing suitable amendments to improve results. |
de.fu_berlin.ties.filter | This packages provides generic filtering functionality and an implementation for sentence filtering. |
Uses of FeatureVector in de.fu_berlin.ties.classify |
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Methods in de.fu_berlin.ties.classify with parameters of type FeatureVector | |
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PredictionDistribution |
TrainableClassifier.classify(FeatureVector features,
Set candidateClasses)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. |
PredictionDistribution |
Classifier.classify(FeatureVector features,
Set candidateClasses)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. |
protected PredictionDistribution |
MetaClassifier.doClassify(FeatureVector features,
Set candidateClasses,
ContextMap context)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. |
protected PredictionDistribution |
OneAgainstTheRestClassifier.doClassify(FeatureVector features,
Set candidateClasses,
ContextMap context)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. This implementation combines the predictions for the positive class of all involved inner classifiers. |
protected PredictionDistribution |
ExternalClassifier.doClassify(FeatureVector features,
Set candidateClasses,
ContextMap context)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. |
protected abstract PredictionDistribution |
TrainableClassifier.doClassify(FeatureVector features,
Set candidateClasses,
ContextMap context)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. |
protected PredictionDistribution |
MultiBinaryClassifier.doClassify(FeatureVector features,
Set candidateClasses,
ContextMap context)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. This implementation combines the predictions for the foreground of all involved inner classifiers. |
protected void |
MetaClassifier.doTrain(FeatureVector features,
String targetClass,
ContextMap context)
Incorporates an item that is represented by a feature vector into the classification model. |
protected void |
OneAgainstTheRestClassifier.doTrain(FeatureVector features,
String targetClass,
ContextMap context)
Incorporates an item that is represented by a feature vector into the classification model. |
protected void |
ExternalClassifier.doTrain(FeatureVector features,
String targetClass,
ContextMap context)
Incorporates an item that is represented by a feature vector into the classification model. |
protected abstract void |
TrainableClassifier.doTrain(FeatureVector features,
String targetClass,
ContextMap context)
Incorporates an item that is represented by a feature vector into the classification model. |
protected void |
MultiBinaryClassifier.doTrain(FeatureVector features,
String targetClass,
ContextMap context)
Incorporates an item that is represented by a feature vector into the classification model. |
void |
TrainableClassifier.train(FeatureVector features,
String targetClass)
Incorporates an item that is represented by a feature vector into the classification model. |
PredictionDistribution |
TrainableClassifier.trainOnError(FeatureVector features,
String targetClass,
Set candidateClasses)
Handles error-driven learning ("train only errors"): the specified feature vector is trained into the model only if the predicted class for the feature vector differs from the specified target class. |
protected boolean |
MetaClassifier.trainOnErrorHook(PredictionDistribution predDist,
FeatureVector features,
String targetClass,
Set candidateClasses,
ContextMap context)
Subclasses can implement this hook for more refined error-driven learning. |
protected boolean |
OneAgainstTheRestClassifier.trainOnErrorHook(PredictionDistribution predDist,
FeatureVector features,
String targetClass,
Set candidateClasses,
ContextMap context)
Subclasses can implement this hook for more refined error-driven learning. |
protected boolean |
TrainableClassifier.trainOnErrorHook(PredictionDistribution predDist,
FeatureVector features,
String targetClass,
Set candidateClasses,
ContextMap context)
Subclasses can implement this hook for more refined error-driven learning. |
protected boolean |
MultiBinaryClassifier.trainOnErrorHook(PredictionDistribution predDist,
FeatureVector features,
String targetClass,
Set candidateClasses,
ContextMap context)
Subclasses can implement this hook for more refined error-driven learning. |
Uses of FeatureVector in de.fu_berlin.ties.classify.feature |
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Subclasses of FeatureVector in de.fu_berlin.ties.classify.feature | |
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class |
DefaultFeatureVector
This feature vector implementation stores an ordered sequence of features. |
class |
FeatureSet
This feature vector implementation stores a multi-set of features. |
Methods in de.fu_berlin.ties.classify.feature that return FeatureVector | |
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protected FeatureVector |
OSBTransformer.doTransform(FeatureVector orgFeatures)
Transforms a feature vector. |
protected FeatureVector |
SBPHTransformer.doTransform(FeatureVector orgFeatures)
Transforms a feature vector. |
protected abstract FeatureVector |
FeatureTransformer.doTransform(FeatureVector orgFeatures)
Transforms a feature vector. |
FeatureVector |
FeatureTransformer.transform(FeatureVector orgFeatures)
Transforms a feature vector. |
Methods in de.fu_berlin.ties.classify.feature with parameters of type FeatureVector | |
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void |
FeatureVector.addAll(FeatureVector fv)
Adds all of the features in the specified feature vector to this vector, in the order they are returned by the specified feature vector. |
protected FeatureVector |
OSBTransformer.doTransform(FeatureVector orgFeatures)
Transforms a feature vector. |
protected FeatureVector |
SBPHTransformer.doTransform(FeatureVector orgFeatures)
Transforms a feature vector. |
protected abstract FeatureVector |
FeatureTransformer.doTransform(FeatureVector orgFeatures)
Transforms a feature vector. |
FeatureVector |
FeatureTransformer.transform(FeatureVector orgFeatures)
Transforms a feature vector. |
Uses of FeatureVector in de.fu_berlin.ties.classify.winnow |
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Methods in de.fu_berlin.ties.classify.winnow with parameters of type FeatureVector | |
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protected PredictionDistribution |
Winnow.doClassify(FeatureVector features,
Set candidateClasses,
ContextMap context)
Classifies an item that is represented by a feature vector by choosing the most probable class among a set of candidate classes. |
protected void |
Winnow.doTrain(FeatureVector features,
String targetClass,
ContextMap context)
Winnow supports only error-driven training, so you always have to use the TrainableClassifier.trainOnError(FeatureVector, String, Set) method
instead of this one. |
protected FeatureSet |
Winnow.featureSet(FeatureVector fv)
Converts a feature vector into a FeatureSet (a multi-set of
features). |
protected boolean |
Winnow.trainOnErrorHook(PredictionDistribution predDist,
FeatureVector features,
String targetClass,
Set candidateClasses,
ContextMap context)
Hook implementing error-driven learning, promoting and demoting weights as required. |
Uses of FeatureVector in de.fu_berlin.ties.context |
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Methods in de.fu_berlin.ties.context that return FeatureVector | |
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FeatureVector |
Representation.buildContext(Document document,
PriorRecognitions priorRecognitions,
Map<Element,List<LocalFeature>> featureCache,
String logPurpose)
Builds the context representation of a document. |
FeatureVector |
Representation.buildContext(Element element,
PriorRecognitions priorRecognitions,
Map<Element,List<LocalFeature>> featureCache,
String logPurpose)
Builds the context representation of an element. |
abstract FeatureVector |
Representation.buildContext(Element element,
String leftText,
String mainText,
String rightText,
PriorRecognitions priorRecognitions,
Map<Element,List<LocalFeature>> featureCache,
String logPurpose)
Builds the context representation of text in an element. |
FeatureVector |
AbstractRepresentation.buildContext(Element element,
String leftText,
String mainText,
String rightText,
PriorRecognitions priorRecognitions,
Map<Element,List<LocalFeature>> featureCache,
String logPurpose)
Builds the context representation of text in an element. |
protected FeatureVector |
DefaultRepresentation.doBuildContext(Element element,
String leftText,
String mainText,
String rightText,
PriorRecognitions priorRecognitions,
Map<Element,List<LocalFeature>> featureCache,
String logPurpose)
Builds the context representation of text in an element. |
protected abstract FeatureVector |
AbstractRepresentation.doBuildContext(Element element,
String leftText,
String mainText,
String rightText,
PriorRecognitions priorRecognitions,
Map<Element,List<LocalFeature>> featureCache,
String logPurpose)
Builds the context representation of text in an element. |
protected FeatureVector |
SimpleRepresentation.doBuildContext(Element element,
String leftText,
String mainText,
String rightText,
PriorRecognitions priorRecognitions,
Map featureCache,
String logPurpose)
Builds the context representation of text in an element. |
Methods in de.fu_berlin.ties.context with parameters of type FeatureVector | |
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protected void |
SimpleRepresentation.addFeature(FeatureVector features,
String prefix,
String value)
Creates a feature and adds it to a feature vector. |
protected void |
SimpleRepresentation.addText(FeatureVector features,
String prefix,
String text)
Adds feature(s) representing text to a feature vector, using the instance tokenizer for splitting the text into tokens. |
protected List<Feature> |
DefaultRepresentation.filterRepresentation(FeatureVector originalRep)
Creates a filtered view of a context representation. |
Uses of FeatureVector in de.fu_berlin.ties.eval |
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Methods in de.fu_berlin.ties.eval with parameters of type FeatureVector | |
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void |
FeatureCount.update(FeatureVector features)
Evaluates a feature vector and updates the statistics accordingly. |
Uses of FeatureVector in de.fu_berlin.ties.extract |
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Methods in de.fu_berlin.ties.extract that return FeatureVector | |
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protected FeatureVector |
ExtractorBase.getFeatures()
Returns vector of features representing the currently processed element. |
Uses of FeatureVector in de.fu_berlin.ties.extract.amend |
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Methods in de.fu_berlin.ties.extract.amend that return FeatureVector | |
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FeatureVector |
ContextDetails.getContext()
Returns a feature vector representing the context of the token. |
Constructors in de.fu_berlin.ties.extract.amend with parameters of type FeatureVector | |
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ContextDetails(String theToken,
int tokenRep,
int tokenIndex,
boolean wsBefore,
FeatureVector features)
Creates a new instance. |
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ContextDetails(TokenDetails orgDetails,
FeatureVector features)
Creates a new instance, re-using field values from a TokenDetails
instance. |
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TrainingContextDetails(String theToken,
int tokenRep,
int tokenIndex,
boolean wsBefore,
FeatureVector features,
CombinationState predicted,
CombinationState expected)
Creates a new instance. |
|
TrainingContextDetails(TokenDetails orgDetails,
FeatureVector features,
CombinationState predicted,
CombinationState expected)
Creates a new instance, re-using field values from a TokenDetails
instance. |
Uses of FeatureVector in de.fu_berlin.ties.filter |
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Methods in de.fu_berlin.ties.filter that return FeatureVector | |
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FeatureVector |
RepresentationFilter.buildFeatures(Element element)
Converts an element into a feature vector to be used by the trainable classifier. |
abstract FeatureVector |
TrainableFilter.buildFeatures(Element element)
Converts an element into a feature vector to be used by the trainable classifier. |
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