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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
AdaptiveFeatureGenerator
s and calls them to generate the features.Factory.getAlphanumeric(String)
InputStream
.Set<String>
.
Attributes
class stores name value pairs.AdaptiveFeatureGenerator
s.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 ChunkSample
s.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.
ChunkSampleStream
s.AdaptiveFeatureGenerator.clearAdaptiveData()
method
on all aggregated AdaptiveFeatureGenerator
s.
InputStream
cannot be closed.
ObjectStream
and releases all allocated
resources.
StringList
contains the
given token.
Entry
s from the given InputStream
and
forwards these Entry
s 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 AdaptiveFeatureGenerator
s.
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
DocumentSample
s.DocumentCategorizer
.DocumentCategorizerME.DocumentCategorizerME(DoccatModel)
instead.
DocumentCategorizerME.DocumentCategorizerME(DoccatModel, FeatureGenerator...)
instead.
DocumentSample
objects.DocumentSampleStream
s.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.ObjectStream
s.FMeasure
is an utility class for evaluators
which measure precision, recall and the resulting f-measure.DocumentCategorizer
.
Attributes
.
Collections
of all aggregated
AdaptiveFeatureGenerator
s.
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)
.
StringList
s.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.
MaxentModel
s.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 String
s
provided by a DataStream
to NameSample
objects.NameSampleDataStream
s.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.
Object
s from a stream.Version
initialized to the value
represented by the specified String
ParserModel
implementations.ParseSampleStream
s.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
POSSample
s.POSModel
is the model used
by a learnable POSTagger
.POSModel.POSModel(String, AbstractModel, Map, POSTaggerFactory)
instead.
POSModel.POSModel(String, AbstractModel, Map, POSTaggerFactory)
instead.
POSSample
s from the given Iterator
and converts the POSSample
s into Event
s 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
SentenceSample
s.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.SentenceSampleStream
s.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.
Span
s to an array of String
s.
StringList
is an immutable list of String
s. tag(String[])
instead
tag(String[]) instead
use WhiteSpaceTokenizer.INSTANCE.tokenize
to obtain the String array.
StringList
which
are in the current NGramModel
.
StringList
s 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
TokenSample
s.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 TokenSample
s.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 NameSample
s.TokenNameFinder
.
TokenNameFinderModel
is the model used
by a learnable TokenNameFinder
.TokenSample
is text with token spans.TokenSample
s out of them.TokenSampleStream
s.TokenSample
s from the given Iterator
and converts the TokenSample
s into Event
s 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 AdaptiveFeatureGenerator
s.
Version
class represents the OpenNlp Tools library version.TokenSample
s into whitespace
separated token strings.AdaptiveFeatureGenerator
.POSSample
objects.OutputStream
.
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