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EventStream classes.Iterator.
Linker that
most implementations of Linker will want to extend.MentionFinder interface.Parse interface.Resolver interface.NameSample stream from a line stream, i.e.
NameSample stream from a InputStream
AdditionalContextFeatureGenerator generates the context from the passed
in additional context.NameSample stream from a line stream, i.e.
NameSample stream from a InputStream
POSSample stream from a line stream, i.e.
POSSample stream from a InputStream
SentenceSample stream from a line stream, i.e.
SentenceSample stream from a FileInputStream
AggregatedFeatureGenerator aggregates a set of
AdaptiveFeatureGenerators and calls them to generate the features.Factory.getAlphanumeric(String)
InputStream.Set<String>.
Attributes class stores name value pairs.AdaptiveFeatureGenerators.CharacterNgramFeatureGenerator uses character ngrams to
generate features about each token.Chunker.chunk(String[], String[]) instead.
ChunkerCrossValidator.ChunkerCrossValidator(String, TrainingParameters, ChunkerFactory, ChunkerEvaluationMonitor...)
instead.
ChunkerCrossValidator.ChunkerCrossValidator(String, TrainingParameters, ChunkerFactory, ChunkerEvaluationMonitor...) instead.
ChunkerEvaluator measures the performance
of the given Chunker with the provided
reference ChunkSamples.Chunker.
ChunkerEventStream.ChunkerEventStream(ObjectStream, ChunkerContextGenerator) instead.
ChunkerFactory that provides the default implementation
of the resources.
ChunkerME.ChunkerME(ChunkerModel, int) instead
and use the ChunkerFactory to configure the SequenceValidator and ChunkerContextGenerator.
ChunkerME.ChunkerME(ChunkerModel, int) instead
and use the ChunkerFactory to configure the SequenceValidator.
ChunkerModel is the model used
by a learnable Chunker.ChunkerModel.ChunkerModel(String, AbstractModel, Map, ChunkerFactory)
instead.
instead.
ChunkSampleStreams.AdaptiveFeatureGenerator.clearAdaptiveData() method
on all aggregated AdaptiveFeatureGenerators.
InputStream
cannot be closed.
ObjectStream and releases all allocated
resources.
StringList contains the
given token.
Entrys from the given InputStream and
forwards these Entrys to the EntryInserter.
POSDictionary from a provided InputStream.
TokenizerFactory.
AdaptiveFeatureGenerator from an provided XML descriptor.
InputStream.
Map with pairs of keys and objects.
Map with pairs of keys and ArtifactSerializer.
AdaptiveFeatureGenerator.
AdaptiveFeatureGenerator.createFeatures(List, String[], int, String[])
method on all aggregated AdaptiveFeatureGenerators.
ObjectStream form an array.
ObjectStream form a collection.
TrainingSampleStream which iterates over
all training elements.EndOfSentenceScanner.NonReferentialResolver interface.Parse mapping it to the API specified in Parse.SDContextGenerator instance with
no induced abbreviations.
SDContextGenerator instance which uses
the set of induced abbreviations.
Dictionary.
Dictionary from an existing dictionary resource.
Dictionary.Dictionary(InputStream) instead and set the
case sensitivity during the dictionary creation.
DictionaryFeatureGenerator uses the DictionaryNameFinder
to generated features for detected names based on the InSpanGenerator.DocumentCategorizerEvaluator measures the performance of
the given DocumentCategorizer with the provided reference
DocumentSamples.DocumentCategorizer.DocumentCategorizerME.DocumentCategorizerME(DoccatModel) instead.
DocumentCategorizerME.DocumentCategorizerME(DoccatModel, FeatureGenerator...) instead.
DocumentSample objects.DocumentSampleStreams.Entry is a StringList which can
optionally be mapped to attributes.DocumentSample objects from the stream
and evaluates each DocumentSample object with
DocumentCategorizerEvaluator.evaluteSample(DocumentSample) method.
Evaluator.evaluateSample(Object) method.
Evaluator is an abstract base class for evaluators.DocumentSample object.
ExtensionLoader is responsible to load extensions to the OpenNLP library.TokenClassFeatureGenerator instead!AdditionalContextFeatureGenerator to make implementing feature generators
easier.FeatureGeneratorResourceProvider provides access to the resources
provided in the model.ObjectStreams.FMeasure is an utility class for evaluators
which measure precision, recall and the resulting f-measure.DocumentCategorizer.
Attributes.
Collections of all aggregated
AdaptiveFeatureGenerators.
Parse.
Parse.
p.
TokenNameFinder model.
Parse.
POSModel.getFactory() to get a
POSTaggerFactory and
POSTaggerFactory.getTagDictionary() to get a
TagDictionary.
ObjectStream over the test/evaluations
elements and poisons this TrainingSampleStream.
AbstractTokenizer.tokenize(String) or TokenizerME.tokenizePos(String).
StringLists.StringList Iterator.
TokenNameFinder.WhitespaceTokenizer.
CharSequence.length() is
0 or null.
Iterator over all StringList entries.
Iterator over all tokens.
getMentionFinder,
and creating entities out of those mentions, getEntities.List as the underlying
data structure.Resolver class and use maximum entropy models to make resolution decisions.Mean.add(double) method.Mean.add(double) or 0 if there are zero added
values.
MaxentModels.TagDictionary entries to be added and removed.InputStream and a Charset
and opens an associated stream object with the specified encoding specified.
NameSampleDataStream class converts tagged Strings
provided by a DataStream to NameSample objects.NameSampleDataStreams.NGramModel can be used to crate ngrams and character ngrams.
[NP Rockwell_NNP ] [VP said_VBD ] [NP the_DT agreement_NN ] [VP calls_VBZ ] [SBAR for_IN ] [NP it_PRP ] [VP to_TO supply_VB ] [NP 200_CD additional_JJ so-called_JJ shipsets_NNS ] [PP for_IN ] [NP the_DT planes_NNS ] ._.
- NO_SPLIT -
Static variable in class opennlp.tools.sentdetect.SentenceDetectorME
- Constant indicates no sentence split.
- NO_SPLIT -
Static variable in class opennlp.tools.tokenize.TokenizerME
- Constant indicates no token split.
- NON_ATTACH -
Static variable in class opennlp.tools.parser.treeinsert.Parser
- Outcome used when a node should not be attached to another node.
- NonReferentialResolver - Interface in opennlp.tools.coref.resolver
- Provides the interface for a object to provide a resolver with a non-referential
probability.
- NP -
Static variable in interface opennlp.tools.coref.Linker
- String constant used to label a mention which consists of a single noun phrase.
- Number - Class in opennlp.tools.coref.sim
- Class which models the number of an entity and the confidence of that association.
- Number(NumberEnum, double) -
Constructor for class opennlp.tools.coref.sim.Number
-
- numberDist(Context) -
Method in class opennlp.tools.coref.sim.NumberModel
-
- numberDist(Context) -
Method in interface opennlp.tools.coref.sim.TestNumberModel
-
- NumberEnum - Class in opennlp.tools.coref.sim
- Enumeration of number types.
- NumberModel - Class in opennlp.tools.coref.sim
- Class which models the number of particular mentions and the entities made up of mentions.
- numberOfGrams() -
Method in class opennlp.tools.ngram.NGramModel
- Retrieves the total count of all Ngrams.
Objects from a stream.Version initialized to the value
represented by the specified String
ParserModel implementations.ParseSampleStreams.String object.POSDictionary.
POSDictionary.
POSDictionary.create(InputStream) instead, old format might removed.
POSDictionary.create(InputStream) instead, old format might removed.
POSDictionary.create(InputStream) instead, old format might removed.
POSDictionary.create(InputStream) instead, old format might removed.
POSEvaluator measures the performance of
the given POSTagger with the provided reference
POSSamples.POSModel is the model used
by a learnable POSTagger.POSModel.POSModel(String, AbstractModel, Map, POSTaggerFactory)
instead.
POSModel.POSModel(String, AbstractModel, Map, POSTaggerFactory)
instead.
POSSamples from the given Iterator
and converts the POSSamples into Events which
can be used by the maxent library for training.POSContextGenerator.
DefaultPOSContextGenerator.
POSTaggerCrossValidator that builds a ngram dictionary
dynamically.
POSTaggerCrossValidator using the given
POSTaggerFactory.
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a TrainingParameters object and a
POSTaggerFactory.
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a TrainingParameters object and a
POSTaggerFactory.
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a POSTaggerFactory.
#POSTaggerCrossValidator(String, TrainingParameters, POSDictionary, Integer, String, POSTaggerEvaluationMonitor...)
instead and pass in the name of POSTaggerFactory
sub-class.
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a POSTaggerFactory.
POSTaggerFactory that provides the default implementation
of the resources.
POSTaggerFactory.
POSTaggerME.POSTaggerME(POSModel, int, int) instead. The model
knows which SequenceValidator to use.
POSTaggerME.train(String, ObjectStream, opennlp.tools.util.model.ModelType, POSDictionary, Dictionary, int, int) instead.FeatureGeneratorAdapter generates features indicating the outcome associated with a previously occuring word.POSSample object.
InputStream into a byte array
which is returned
Iterator back to the first retrieved element,
the seen sequence of elements must be repeated.
Iterator resetable.SentenceDetectorME context generators.SDCrossValidator.SDCrossValidator(String, TrainingParameters, SentenceDetectorFactory, SentenceDetectorEvaluationMonitor...)
and pass in a SentenceDetectorFactory.
SDCrossValidator.SDCrossValidator(String, TrainingParameters, SentenceDetectorFactory, SentenceDetectorEvaluationMonitor...)
and pass in a SentenceDetectorFactory.
#SDCrossValidator(String, TrainingParameters, Dictionary, SentenceDetectorEvaluationMonitor...)
instead and pass in a TrainingParameters object.
#SDCrossValidator(String, TrainingParameters, Dictionary, SentenceDetectorEvaluationMonitor...)
instead and pass in a TrainingParameters object.
#SDCrossValidator(String, TrainingParameters, Dictionary, SentenceDetectorEvaluationMonitor...)
instead and pass in a TrainingParameters object.
SentenceDetectorEvaluator measures the performance of
the given SentenceDetector with the provided reference
SentenceSamples.SentenceDetectorFactory that provides the default
implementation of the resources.
SentenceDetectorFactory.
SentenceDetectorFactory to extend
SentenceDetector functionality.
SentenceModel is the model used
by a learnable SentenceDetector.SentenceModel.SentenceModel(String, AbstractModel, Map, SentenceDetectorFactory)
instead and pass in a SentenceDetectorFactory
SentenceModel.SentenceModel(String, AbstractModel, Map, SentenceDetectorFactory)
instead and pass in a SentenceDetectorFactory
SentenceSample contains a document with
begin indexes of the individual sentences.Reader and converts them into SentenceSample objects.SentenceSampleStreams.OutputStream.
DictionarySerializer.serialize(java.io.OutputStream, java.util.Iterator, boolean) instead
OutputStream.
OutputStream.
POSDictionary to the given OutputStream;
After the serialization is finished the provided
OutputStream remains open.
OutputStream.
OutputStream.
StringList entries in the current instance.
Spans to an array of Strings.
StringList is an immutable list of Strings. tag(String[]) instead
tag(String[]) instead use WhiteSpaceTokenizer.INSTANCE.tokenize
to obtain the String array.
StringList which
are in the current NGramModel.
StringLists which
are in the current NGramModel.
TokenizerME context generators.TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory
TokenizerEvaluator measures the performance of
the given Tokenizer with the provided reference
TokenSamples.Tokenizer.
Tokenizer default implementations and
resources.TokenizerFactory that provides the default implementation
of the resources.
TokenizerFactory.
TokenizerFactory to extend the Tokenizer
functionality
TokenizerModel is the model used
by a learnable Tokenizer.TokenizerModel#TokenizerModel(String, AbstractModel, Map, TokenizerFactory)
instead and pass in a TokenizerFactory.
TokenizerModel#TokenizerModel(String, AbstractModel, Map, TokenizerFactory)
instead and pass in a TokenizerFactory.
TokenizerModel#TokenizerModel(String, AbstractModel, Map, TokenizerFactory)
instead and pass in a TokenizerFactory.
TokenizerStream uses a tokenizer to tokenize the
input string and output TokenSamples.TokenNameFinderCrossValidator.TokenNameFinderCrossValidator(String, String, TrainingParameters, byte[], Map, TokenNameFinderEvaluationMonitor...)
instead and pass in a TrainingParameters object.
TokenNameFinderCrossValidator.TokenNameFinderCrossValidator(String, String, TrainingParameters, byte[], Map, TokenNameFinderEvaluationMonitor...)
instead and pass in a TrainingParameters object.
TokenNameFinderCrossValidator.TokenNameFinderCrossValidator(String, String, TrainingParameters, byte[], Map, TokenNameFinderEvaluationMonitor...)
instead and pass in a TrainingParameters object.
TokenNameFinderEvaluator measures the performance
of the given TokenNameFinder with the provided
reference NameSamples.TokenNameFinder.
TokenNameFinderModel is the model used
by a learnable TokenNameFinder.TokenSample is text with token spans.TokenSamples out of them.TokenSampleStreams.TokenSamples from the given Iterator
and converts the TokenSamples into Events which
can be used by the maxent library for training.Character.toLowerCase(char) which uses mapping information
from the UnicodeData file.
Chunker.topKSequences(String[], String[]) instead.
topKSequences(String[]) instead
String.
String.
String representation.
Character.toUpperCase(char) which uses mapping information
from the UnicodeData file.
#train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters, ChunkerFactory)
instead.
ChunkerME.train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters)
instead and pass in a TrainingParameters object.
ChunkerME.train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters)
instead and pass in a TrainingParameters object.
setEntities.
NameFinderME.train(String, String, ObjectStream, TrainingParameters, AdaptiveFeatureGenerator, Map)
instead and pass in a TrainingParameters object.
NameFinderME.train(String, String, ObjectStream, TrainingParameters, byte[], Map)
instead and pass in a TrainingParameters object.
Parser.train(String, ObjectStream, HeadRules, TrainingParameters)
instead and pass in a TrainingParameters object.
POSTaggerME.train(String, ObjectStream, TrainingParameters, POSTaggerFactory)
instead and pass in a POSTaggerFactory.
POSTaggerME.train(String, ObjectStream, TrainingParameters, POSTaggerFactory)
instead and pass in a POSTaggerFactory and a
TrainingParameters.
SentenceDetectorME.train(String, ObjectStream, SentenceDetectorFactory, TrainingParameters)
and pass in af SentenceDetectorFactory.
SentenceDetectorME.train(String, ObjectStream, SentenceDetectorFactory, TrainingParameters)
and pass in af SentenceDetectorFactory.
SentenceDetectorME.train(String, ObjectStream, SentenceDetectorFactory, TrainingParameters)
and pass in af SentenceDetectorFactory.
TokenizerME.
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory
InputStream which cannot be closed.AdaptiveFeatureGenerator.updateAdaptiveData(String[], String[])
method on all aggregated AdaptiveFeatureGenerators.
Version class represents the OpenNlp Tools library version.TokenSamples into whitespace
separated token strings.AdaptiveFeatureGenerator.POSSample objects.OutputStream.
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