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EventStream classes.Iterator.
Linker that
most implementations of Linker will want to extend.MentionFinder interface.Parse interface.Resolver interface.AdditionalContextFeatureGenerator generates the context from the passed
in additional context.AggregatedFeatureGenerator aggregates a set of
AdaptiveFeatureGenerators and calls them to generate the features.InputStream.Attributes class stores name value pairs.AdaptiveFeatureGenerators.NameFinder,
from data collected from US Census data.CharacterNgramFeatureGenerator uses character ngrams to
generate features about each token.Chunker.chunk(String[], String[]) instead.
ChunkerModel is the model used
by a learnable Chunker.AdaptiveFeatureGenerator.clearAdaptiveData() method
on all aggregated AdaptiveFeatureGenerators.
InputStream
cannot be closed.
ObjectStream and releases all allocated
resources.
StringList contains the
given Token.
ObjectStream
Entrys form the given InputStream and
forwards these Entrys to the EntryInserter.
POSDictionary from a provided InputStream.
InputStream.
ArtifactSerializer for their artifact file name extensions.
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.
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.Entry is a StringList which can
optionally be mapped to attributes.DocumentSample objects from the stream
and evaluates each DocumentSample object with
#evaluateSample(POSSample) method.
Evaluator.evaluateSample(Object) method.
NameSample object.
POSSample object.
TokenSample object.
Evaluator is an abstract base class for evaluators.DocumentSample object.
AdditionalContextFeatureGenerator to make implementing feature generators
easier.FeatureGeneratorFactory interface is factory for AdaptiveFeatureGenerators.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.
ObjectStream over the test/evaluations
elements and poisons this TrainingSampleStream.
AbstractTokenizer.tokenize(String) or TokenizerME.tokenizePos(String).
Tokens.
StringLists.StringList Iterator.
TokenNameFinder.WhitespaceTokenizer.
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.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.NGramModel can be used to crate ngrams and character ngrams.Objects from a stream.Version initialized to the value
represented by the specified String
ParserModel implementations.PerformanceMonitor measures increments to a counter.String object.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
POSSamplees.POSModel is the model used
by a learnable POSTagger.POSSamples from the given Iterator
and converts the POSSamples into Events which
can be used by the maxent library for training.POSContextGenerator.
DefaultPOSContextGenerator.
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
UnsupportedOperationException
Iterator back to the first retrieved element,
the seen sequence of elements must be repeated.
Iterator resetable.SentenceDetectorME context generators.SentenceDetectorEvaluator measures the performance of
the given SentenceDetector with the provided reference
SentenceSamples.SentenceModel is the model used
by a learnable SentenceDetector.SentenceSample contains a document with
begin indexes of the individual sentences.Reader and converts them into SentenceSample objects.OutputStream.
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.StringList which
are in the current NGramModel.
StringLists which
are in the current NGramModel.
TokenizerME context generators.TokenizerEvaluator measures the performance of
the given Tokenizer with the provided reference
TokenSamples.Tokenizer.
TokenizerModel is the model used
by a learnable Tokenizer.TokenizerStream uses a tokenizer to tokenize the
input string and output TokenSamples.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.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.
String.
String.
String representation.
Character.toUpperCase(char) which uses mapping information
from the UnicodeData file.
ChunkerME.
setEntities.
TokenizerME.
TokenizerME with a default cutoff of 5 and 100 iterations.
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.BaseModel to disk.
OutputStream.
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