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Deprecated Methods |
opennlp.tools.chunker.ChunkerME.chunk(List, List)
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opennlp.tools.chunker.Chunker.chunk(List, List)
please use Chunker.chunk(String[], String[]) instead. |
opennlp.tools.util.featuregen.FeatureGeneratorFactory.createFeatureGenerator(FeatureGeneratorResourceProvider)
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opennlp.tools.postag.POSModel.getTagDictionary()
Use POSModel.getFactory() to get a
POSTaggerFactory and
POSTaggerFactory.getTagDictionary() to get a
TagDictionary. |
opennlp.tools.postag.POSTaggerTrainer.main(String[])
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opennlp.tools.util.ListHeap.main(String[])
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opennlp.tools.tokenize.SimpleTokenizer.main(String[])
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opennlp.tools.namefind.NameFinderEventStream.main(String[])
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opennlp.tools.namefind.TokenNameFinderEvaluator.main(String[])
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opennlp.tools.parser.Parse.main(String[])
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opennlp.tools.dictionary.serializer.DictionarySerializer.serialize(OutputStream, Iterator)
Use DictionarySerializer.serialize(java.io.OutputStream, java.util.Iterator, boolean) instead |
opennlp.tools.postag.POSTagger.tag(List)
call tag(String[]) instead |
opennlp.tools.postag.POSTaggerME.tag(List)
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opennlp.tools.postag.POSTagger.tag(String)
call tag(String[]) instead use WhiteSpaceTokenizer.INSTANCE.tokenize
to obtain the String array. |
opennlp.tools.postag.POSTaggerME.tag(String)
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opennlp.tools.postag.POSTaggerTrainer.test(AbstractModel)
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opennlp.tools.postag.POSTagger.topKSequences(List)
call topKSequences(String[]) instead |
opennlp.tools.postag.POSTaggerME.topKSequences(List)
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opennlp.tools.chunker.ChunkerME.topKSequences(List, List)
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opennlp.tools.chunker.Chunker.topKSequences(List, List)
please use Chunker.topKSequences(String[], String[]) instead. |
opennlp.tools.doccat.DocumentCategorizerME.train(DocumentCategorizerEventStream)
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opennlp.tools.namefind.NameFinderME.train(EventStream, int, int)
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opennlp.tools.parser.chunking.Parser.train(EventStream, int, int)
Please do not use anymore, use the ObjectStream train methods instead! This method
will be removed soon. |
opennlp.tools.parser.treeinsert.Parser.train(EventStream, int, int)
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opennlp.tools.chunker.ChunkerME.train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters)
Use
#train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters, ChunkerFactory)
instead. |
opennlp.tools.chunker.ChunkerME.train(String, ObjectStream, int, int)
use ChunkerME.train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters)
instead and pass in a TrainingParameters object. |
opennlp.tools.chunker.ChunkerME.train(String, ObjectStream, int, int, ChunkerContextGenerator)
use ChunkerME.train(String, ObjectStream, ChunkerContextGenerator, TrainingParameters)
instead and pass in a TrainingParameters object. |
opennlp.tools.parser.chunking.Parser.train(String, ObjectStream, HeadRules, int, int)
use Parser.train(String, ObjectStream, HeadRules, TrainingParameters)
instead and pass in a TrainingParameters object. |
opennlp.tools.postag.POSTaggerME.train(String, ObjectStream, ModelType, POSDictionary, Dictionary, int, int)
use
POSTaggerME.train(String, ObjectStream, TrainingParameters, POSTaggerFactory)
instead and pass in a POSTaggerFactory and a
TrainingParameters. |
opennlp.tools.postag.POSTaggerME.train(String, ObjectStream, TrainingParameters, POSDictionary, Dictionary)
use
POSTaggerME.train(String, ObjectStream, TrainingParameters, POSTaggerFactory)
instead and pass in a POSTaggerFactory. |
opennlp.tools.sentdetect.SentenceDetectorME.train(String, ObjectStream, boolean, Dictionary)
Use
SentenceDetectorME.train(String, ObjectStream, SentenceDetectorFactory, TrainingParameters)
and pass in af SentenceDetectorFactory. |
opennlp.tools.sentdetect.SentenceDetectorME.train(String, ObjectStream, boolean, Dictionary, int, int)
Use
SentenceDetectorME.train(String, ObjectStream, SentenceDetectorFactory, TrainingParameters)
and pass in af SentenceDetectorFactory. |
opennlp.tools.sentdetect.SentenceDetectorME.train(String, ObjectStream, boolean, Dictionary, TrainingParameters)
Use
SentenceDetectorME.train(String, ObjectStream, SentenceDetectorFactory, TrainingParameters)
and pass in af SentenceDetectorFactory. |
opennlp.tools.tokenize.TokenizerME.train(String, ObjectStream, boolean)
Use
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerME.train(String, ObjectStream, boolean, int, int)
Use
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerME.train(String, ObjectStream, boolean, TrainingParameters)
Use
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerME.train(String, ObjectStream, Dictionary, boolean, TrainingParameters)
Use
#train(String, ObjectStream, TokenizerFactory, TrainingParameters)
and pass in a TokenizerFactory |
opennlp.tools.namefind.NameFinderME.train(String, String, ObjectStream, byte[], Map, int, int)
use NameFinderME.train(String, String, ObjectStream, TrainingParameters, byte[], Map)
instead and pass in a TrainingParameters object. |
opennlp.tools.namefind.NameFinderME.train(String, String, ObjectStream, Map, int, int)
use NameFinderME.train(String, String, ObjectStream, TrainingParameters, AdaptiveFeatureGenerator, Map)
instead and pass in a TrainingParameters object. |
opennlp.tools.postag.POSTaggerTrainer.trainMaxentModel(EventStream, File)
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opennlp.tools.postag.POSTaggerTrainer.trainMaxentModel(EventStream, int, int)
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opennlp.tools.util.CountedSet.write(String, int)
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opennlp.tools.util.CountedSet.write(String, int, String)
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opennlp.tools.util.CountedSet.write(String, int, String, String)
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Deprecated Constructors |
opennlp.tools.chunker.ChunkerCrossValidator(String, int, int)
Use
ChunkerCrossValidator.ChunkerCrossValidator(String, TrainingParameters, ChunkerFactory, ChunkerEvaluationMonitor...)
instead. |
opennlp.tools.chunker.ChunkerCrossValidator(String, TrainingParameters, ChunkerEvaluationMonitor...)
Use ChunkerCrossValidator.ChunkerCrossValidator(String, TrainingParameters, ChunkerFactory, ChunkerEvaluationMonitor...) instead. |
opennlp.tools.chunker.ChunkerEventStream(ObjectStream)
Use ChunkerEventStream.ChunkerEventStream(ObjectStream, ChunkerContextGenerator) instead. |
opennlp.tools.chunker.ChunkerME(ChunkerModel, int, SequenceValidator)
Use ChunkerME.ChunkerME(ChunkerModel, int) instead
and use the ChunkerFactory to configure the SequenceValidator. |
opennlp.tools.chunker.ChunkerME(ChunkerModel, int, SequenceValidator, ChunkerContextGenerator)
Use ChunkerME.ChunkerME(ChunkerModel, int) instead
and use the ChunkerFactory to configure the SequenceValidator and ChunkerContextGenerator. |
opennlp.tools.chunker.ChunkerME(MaxentModel)
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opennlp.tools.chunker.ChunkerME(MaxentModel, ChunkerContextGenerator)
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opennlp.tools.chunker.ChunkerME(MaxentModel, ChunkerContextGenerator, int)
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opennlp.tools.chunker.ChunkerModel(String, AbstractModel)
Use
instead. |
opennlp.tools.chunker.ChunkerModel(String, AbstractModel, Map)
Use
ChunkerModel.ChunkerModel(String, AbstractModel, Map, ChunkerFactory)
instead. |
opennlp.tools.namefind.DefaultNameContextGenerator()
use the other constructor and always provide the feature generators |
opennlp.tools.dictionary.Dictionary(InputStream, boolean)
This constructor is deprecated. Passing the case sensitivity
flag has no effect. Use
Dictionary.Dictionary(InputStream) instead and set the
case sensitivity during the dictionary creation. |
opennlp.tools.doccat.DocumentCategorizerME(MaxentModel)
Use DocumentCategorizerME.DocumentCategorizerME(DoccatModel) instead. |
opennlp.tools.doccat.DocumentCategorizerME(MaxentModel, FeatureGenerator...)
Use DocumentCategorizerME.DocumentCategorizerME(DoccatModel, FeatureGenerator...) instead. |
opennlp.tools.parser.lang.en.HeadRules(String)
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opennlp.tools.namefind.NameFinderME(MaxentModel)
Use the new model API! |
opennlp.tools.namefind.NameFinderME(MaxentModel, NameContextGenerator)
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opennlp.tools.namefind.NameFinderME(MaxentModel, NameContextGenerator, int)
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opennlp.tools.parser.treeinsert.Parser(AbstractModel, AbstractModel, AbstractModel, POSTagger, Chunker, HeadRules)
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opennlp.tools.parser.treeinsert.Parser(AbstractModel, AbstractModel, AbstractModel, POSTagger, Chunker, HeadRules, int, double)
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opennlp.tools.parser.chunking.Parser(MaxentModel, MaxentModel, POSTagger, Chunker, HeadRules)
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opennlp.tools.parser.chunking.Parser(MaxentModel, MaxentModel, POSTagger, Chunker, HeadRules, int, double)
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opennlp.tools.postag.POSDictionary(BufferedReader, boolean)
Use POSDictionary.create(InputStream) instead, old format might removed. |
opennlp.tools.postag.POSDictionary(String)
Use POSDictionary.create(InputStream) instead, old format might removed. |
opennlp.tools.postag.POSDictionary(String, boolean)
Use POSDictionary.create(InputStream) instead, old format might removed. |
opennlp.tools.postag.POSDictionary(String, String, boolean)
Use POSDictionary.create(InputStream) instead, old format might removed. |
opennlp.tools.postag.POSModel(String, AbstractModel, POSDictionary, Dictionary)
Use
POSModel.POSModel(String, AbstractModel, Map, POSTaggerFactory)
instead. |
opennlp.tools.postag.POSModel(String, AbstractModel, POSDictionary, Dictionary, Map)
Use
POSModel.POSModel(String, AbstractModel, Map, POSTaggerFactory)
instead. |
opennlp.tools.postag.POSTaggerCrossValidator(String, ModelType, POSDictionary, Dictionary)
use
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a TrainingParameters object and a
POSTaggerFactory. |
opennlp.tools.postag.POSTaggerCrossValidator(String, ModelType, POSDictionary, Dictionary, int, int)
use
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a TrainingParameters object and a
POSTaggerFactory. |
opennlp.tools.postag.POSTaggerCrossValidator(String, TrainingParameters, POSDictionary, Dictionary, POSTaggerEvaluationMonitor...)
use
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a POSTaggerFactory. |
opennlp.tools.postag.POSTaggerCrossValidator(String, TrainingParameters, POSDictionary, Integer, POSTaggerEvaluationMonitor...)
use
#POSTaggerCrossValidator(String, TrainingParameters, POSDictionary, Integer, String, POSTaggerEvaluationMonitor...)
instead and pass in the name of POSTaggerFactory
sub-class. |
opennlp.tools.postag.POSTaggerCrossValidator(String, TrainingParameters, POSDictionary, POSTaggerEvaluationMonitor...)
use
POSTaggerCrossValidator.POSTaggerCrossValidator(String, TrainingParameters, POSTaggerFactory, POSTaggerEvaluationMonitor...)
instead and pass in a POSTaggerFactory. |
opennlp.tools.postag.POSTaggerME(AbstractModel, Dictionary)
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opennlp.tools.postag.POSTaggerME(AbstractModel, Dictionary, TagDictionary)
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opennlp.tools.postag.POSTaggerME(AbstractModel, POSContextGenerator)
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opennlp.tools.postag.POSTaggerME(AbstractModel, POSContextGenerator, TagDictionary)
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opennlp.tools.postag.POSTaggerME(AbstractModel, TagDictionary)
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opennlp.tools.postag.POSTaggerME(int, AbstractModel, POSContextGenerator, TagDictionary)
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opennlp.tools.postag.POSTaggerME(POSModel, int, int, SequenceValidator)
use POSTaggerME.POSTaggerME(POSModel, int, int) instead. The model
knows which SequenceValidator to use. |
opennlp.tools.sentdetect.SDCrossValidator(String)
use #SDCrossValidator(String, TrainingParameters, Dictionary, SentenceDetectorEvaluationMonitor...)
instead and pass in a TrainingParameters object. |
opennlp.tools.sentdetect.SDCrossValidator(String, int, int)
Use
SDCrossValidator.SDCrossValidator(String, TrainingParameters, SentenceDetectorFactory, SentenceDetectorEvaluationMonitor...)
and pass in a SentenceDetectorFactory. |
opennlp.tools.sentdetect.SDCrossValidator(String, int, int, Dictionary)
use #SDCrossValidator(String, TrainingParameters, Dictionary, SentenceDetectorEvaluationMonitor...)
instead and pass in a TrainingParameters object. |
opennlp.tools.sentdetect.SDCrossValidator(String, TrainingParameters)
Use
SDCrossValidator.SDCrossValidator(String, TrainingParameters, SentenceDetectorFactory, SentenceDetectorEvaluationMonitor...)
and pass in a SentenceDetectorFactory. |
opennlp.tools.sentdetect.SDCrossValidator(String, TrainingParameters, SentenceDetectorEvaluationMonitor...)
use
#SDCrossValidator(String, TrainingParameters, Dictionary, SentenceDetectorEvaluationMonitor...)
instead and pass in a TrainingParameters object. |
opennlp.tools.sentdetect.SentenceDetectorME(SentenceModel, Factory)
Use a SentenceDetectorFactory to extend
SentenceDetector functionality. |
opennlp.tools.sentdetect.SentenceModel(String, AbstractModel, boolean, Dictionary, char[])
Use
SentenceModel.SentenceModel(String, AbstractModel, Map, SentenceDetectorFactory)
instead and pass in a SentenceDetectorFactory |
opennlp.tools.sentdetect.SentenceModel(String, AbstractModel, boolean, Dictionary, char[], Map)
Use
SentenceModel.SentenceModel(String, AbstractModel, Map, SentenceDetectorFactory)
instead and pass in a SentenceDetectorFactory |
opennlp.tools.tokenize.SimpleTokenizer()
Use INSTANCE field instead to obtain an instance, constructor
will be made private in the future. |
opennlp.tools.tokenize.TokenizerCrossValidator(String, boolean)
use
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerCrossValidator(String, boolean, int, int)
use
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerCrossValidator(String, boolean, TrainingParameters, TokenizerEvaluationMonitor...)
use
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerCrossValidator(String, Dictionary, boolean, TrainingParameters, TokenizerEvaluationMonitor...)
use
TokenizerCrossValidator.TokenizerCrossValidator(TrainingParameters, TokenizerFactory, TokenizerEvaluationMonitor...)
instead and pass in a TokenizerFactory |
opennlp.tools.tokenize.TokenizerME(TokenizerModel, Factory)
use TokenizerFactory to extend the Tokenizer
functionality |
opennlp.tools.tokenize.TokenizerModel(String, AbstractModel, boolean)
Use
TokenizerModel#TokenizerModel(String, AbstractModel, Map, TokenizerFactory)
instead and pass in a TokenizerFactory. |
opennlp.tools.tokenize.TokenizerModel(String, AbstractModel, boolean, Map)
Use
TokenizerModel#TokenizerModel(String, AbstractModel, Map, TokenizerFactory)
instead and pass in a TokenizerFactory. |
opennlp.tools.tokenize.TokenizerModel(String, AbstractModel, Dictionary, boolean, Map)
Use
TokenizerModel#TokenizerModel(String, AbstractModel, Map, TokenizerFactory)
instead and pass in a TokenizerFactory. |
opennlp.tools.namefind.TokenNameFinderCrossValidator(String, int, int)
use TokenNameFinderCrossValidator.TokenNameFinderCrossValidator(String, String, TrainingParameters, byte[], Map, TokenNameFinderEvaluationMonitor...)
instead and pass in a TrainingParameters object. |
opennlp.tools.namefind.TokenNameFinderCrossValidator(String, String, byte[], Map, int, int)
use TokenNameFinderCrossValidator.TokenNameFinderCrossValidator(String, String, TrainingParameters, byte[], Map, TokenNameFinderEvaluationMonitor...)
instead and pass in a TrainingParameters object. |
opennlp.tools.namefind.TokenNameFinderCrossValidator(String, String, int, int)
use TokenNameFinderCrossValidator.TokenNameFinderCrossValidator(String, String, TrainingParameters, byte[], Map, TokenNameFinderEvaluationMonitor...)
instead and pass in a TrainingParameters object. |