AbbrAnnotation() CoreAnnotations.AbbrAnnotation
AbbreviationMap(boolean) RobustTokenizer.AbbreviationMap
Creates a new instance of AbreviationMap with some know abbreviations
AbgeneAnnotation() CoreAnnotations.AbgeneAnnotation
about() TregexGUI
aboveLineSkip TreeJPanel
absoluteDifference(Counter<E>, Counter<E>) Counters
Returns |c1 - c2|.
absolutelyDiscountedDistribution(Counter<E>, int, double) Distribution
AbstractCachingDiffFloatFunction edu.stanford.nlp.optimization
AbstractCachingDiffFloatFunction() AbstractCachingDiffFloatFunction
AbstractCachingDiffFunction edu.stanford.nlp.optimization
A differentiable function that caches the last evaluation of its value and
derivative.
AbstractCachingDiffFunction() AbstractCachingDiffFunction
AbstractCollinsHeadFinder edu.stanford.nlp.trees
A base class for a HeadFinder similar to the one described in
Michael Collins' 1999 thesis.
AbstractCollinsHeadFinder(TreebankLanguagePack, String...) AbstractCollinsHeadFinder
Construct a HeadFinder.
AbstractCoreLabel edu.stanford.nlp.ling
AbstractCounter E > - Class in edu.stanford.nlp.stats
Default implementations of all the convenience methods provided by
Counter.
AbstractCounter() AbstractCounter
AbstractDataset edu.stanford.nlp.trees.treebank
AbstractDataset() AbstractDataset
AbstractDataset.SplitFilter edu.stanford.nlp.trees.treebank
AbstractDependencyGrammar edu.stanford.nlp.parser.lexparser
An abstract base class for dependency grammars.
AbstractDependencyGrammar(TreebankLanguagePack, TagProjection, boolean, boolean, boolean, Options, Index<String>, Index<String>) AbstractDependencyGrammar
AbstractEval edu.stanford.nlp.parser.metrics
A framework for Set-based precision/recall/F1 evaluation.
AbstractEval() AbstractEval
AbstractEval(boolean) AbstractEval
AbstractEval(String) AbstractEval
AbstractEval(String, boolean) AbstractEval
AbstractEval.CatErrorEval edu.stanford.nlp.parser.metrics
This class counts which categories are over and underproposed in trees.
AbstractEval.RuleErrorEval edu.stanford.nlp.parser.metrics
AbstractEval.ScoreEval edu.stanford.nlp.parser.metrics
This isn't really a kind of AbstractEval: we're sort of cheating here.
AbstractEvaluate edu.stanford.nlp.sentiment
AbstractEvaluate(RNNOptions) AbstractEvaluate
AbstractEvaluator() EvalUtils.AbstractEvaluator
AbstractIterator E > - Class in edu.stanford.nlp.util
Iterator with remove() defined to throw an
UnsupportedOperationException.
AbstractIterator() AbstractIterator
AbstractLinearClassifierFactory L ,F > - Class in edu.stanford.nlp.classify
AbstractLinearClassifierFactory() AbstractLinearClassifierFactory
AbstractListProcessor IN ,OUT ,L ,F > - Class in edu.stanford.nlp.process
Class AbstractListProcessor
AbstractListProcessor() AbstractListProcessor
AbstractMatchCostFunction() MatchCostFunction.AbstractMatchCostFunction
AbstractSequenceClassifier IN extends CoreMap > - Class in edu.stanford.nlp.ie
This class provides common functionality for (probabilistic) sequence models.
AbstractSequenceClassifier(Properties) AbstractSequenceClassifier
Construct a SeqClassifierFlags object based on the passed in properties,
and then call the other constructor.
AbstractSequenceClassifier(SeqClassifierFlags) AbstractSequenceClassifier
Initialize the featureFactory and other variables based on the passed in
flags.
AbstractStochasticCachingDiffFunction edu.stanford.nlp.optimization
AbstractStochasticCachingDiffFunction() AbstractStochasticCachingDiffFunction
AbstractStochasticCachingDiffFunction.SamplingMethod edu.stanford.nlp.optimization
AbstractStochasticCachingDiffUpdateFunction edu.stanford.nlp.optimization
Function for stochastic calculations that does update in place
(instead of maintaining and returning the derivative).
AbstractStochasticCachingDiffUpdateFunction() AbstractStochasticCachingDiffUpdateFunction
AbstractStringAnnotationPattern() ComplexNodePattern.AbstractStringAnnotationPattern
AbstractTextAnnotationCreator edu.stanford.nlp.pipeline
Creates a stub implementation for creating annotation from
various input sources using String as the main input source
AbstractTextAnnotationCreator() AbstractTextAnnotationCreator
AbstractTokenizer T > - Class in edu.stanford.nlp.process
An abstract tokenizer.
AbstractTokenizer() AbstractTokenizer
AbstractTreebankLanguagePack edu.stanford.nlp.trees
This provides an implementation of parts of the TreebankLanguagePack
API to reduce the load on fresh implementations.
AbstractTreebankLanguagePack() AbstractTreebankLanguagePack
Gives a handle to the TreebankLanguagePack.
AbstractTreebankLanguagePack(char) AbstractTreebankLanguagePack
Gives a handle to the TreebankLanguagePack.
AbstractTreebankParserParams edu.stanford.nlp.parser.lexparser
An abstract class providing a common method base from which to
complete a TreebankLangParserParams implementing class.
AbstractTreebankParserParams(TreebankLanguagePack) AbstractTreebankParserParams
Stores the passed-in TreebankLanguagePack and sets up charset encodings.
AbstractTreebankParserParams.AnnotatePunctuationFunction edu.stanford.nlp.parser.lexparser
Annotation function for mapping punctuation to PTB-style equivalence classes.
AbstractTreebankParserParams.RemoveGFSubcategoryStripper edu.stanford.nlp.parser.lexparser
The job of this class is to remove subcategorizations from
tag and category nodes, so as to put a tree in a suitable
state for evaluation.
AbstractTreebankParserParams.SubcategoryStripper edu.stanford.nlp.parser.lexparser
The job of this class is to remove subcategorizations from
tag and category nodes, so as to put a tree in a suitable
state for evaluation.
AbstractTreeExtractor T > - Class in edu.stanford.nlp.parser.lexparser
An abstract superclass for parser classes that extract counts from Trees.
AbstractTreeExtractor(Options) AbstractTreeExtractor
AbstractUnknownWordModelTrainer edu.stanford.nlp.parser.lexparser
AbstractUnknownWordModelTrainer() AbstractUnknownWordModelTrainer
AbstrAnnotation() CoreAnnotations.AbstrAnnotation
accept(File) ExtensionFileFilter
Checks whether a file satisfies the selection filter.
ACCEPT Lexer
accept(File) NumberRangeFileFilter
Checks whether a file satisfies the number range selection filter.
accept(File) NumberRangesFileFilter
Checks whether a file satisfies the number range selection filter.
accept(String) NumberRangesFileFilter
Checks whether a String satisfies the number range selection filter.
accept(File) RegExFileFilter
Checks whether a file satisfies the selection filter.
accept(File) AbstractDataset.SplitFilter
acceptFilter() Filters
The acceptFilter accepts everything.
accepting DFSAState
accuracy(Iterator<RVFDatum<L, F>>) NaiveBayesClassifier
accuracy() PRCurve
AccuracyStats L > - Class in edu.stanford.nlp.stats
Utility class for aggregating counts of true positives, false positives, and
false negatives and computing precision/recall/F1 stats.
AccuracyStats(ProbabilisticClassifier<L, F>, GeneralDataset<L, F>, L) AccuracyStats
AccuracyStats(L, String) AccuracyStats
ACE2004_PROP Constants
ACE2005_PROP Constants
AceCharSeq edu.stanford.nlp.ie.machinereading.domains.ace.reader
Implements the ACE <charseq> construct.
AceCharSeq(String, int, int) AceCharSeq
Token that incorporates this whole char sequence, e.g.
AceDocument edu.stanford.nlp.ie.machinereading.domains.ace.reader
Stores the ACE elements annotated in this document
AceDocument(String) AceDocument
AceDomReader edu.stanford.nlp.ie.machinereading.domains.ace.reader
DOM reader for an ACE specification.
AceDomReader() AceDomReader
AceElement edu.stanford.nlp.ie.machinereading.domains.ace.reader
Base class for all ACE annotation elements.
AceElement(String) AceElement
AceEntity edu.stanford.nlp.ie.machinereading.domains.ace.reader
Implements the ACE <entity> construct.
AceEntity(String, String, String, String) AceEntity
AceEntityMention edu.stanford.nlp.ie.machinereading.domains.ace.reader
Implements the ACE construct
AceEntityMention(String, String, String, AceCharSeq, AceCharSeq) AceEntityMention
AceEvent edu.stanford.nlp.ie.machinereading.domains.ace.reader
Stores one ACE event
AceEvent(String, String, String, String, String, String, String) AceEvent
AceEventMention edu.stanford.nlp.ie.machinereading.domains.ace.reader
Stores one ACE event mention
AceEventMention(String, AceCharSeq, AceCharSeq) AceEventMention
AceEventMentionArgument edu.stanford.nlp.ie.machinereading.domains.ace.reader
AceEventMentionArgument(String, AceEntityMention) AceEventMentionArgument
AceMention edu.stanford.nlp.ie.machinereading.domains.ace.reader
AceMention(String, AceCharSeq) AceMention
AceMentionArgument edu.stanford.nlp.ie.machinereading.domains.ace.reader
AceMentionArgument(String, AceEntityMention, String) AceMentionArgument
ACEMentionExtractor edu.stanford.nlp.dcoref
Extracts <COREF> mentions from a file annotated in ACE format (ACE2004, ACE2005).
ACEMentionExtractor(Dictionaries, Properties, Semantics) ACEMentionExtractor
ACEMentionExtractor(Dictionaries, Properties, Semantics, LogisticClassifier<String, String>) ACEMentionExtractor
AceReader edu.stanford.nlp.ie.machinereading.domains.ace
Simple wrapper of Mihai's ACE code to ie.machinereading.structure objects.
AceReader() AceReader
Make an AceReader.
AceReader(StanfordCoreNLP, boolean) AceReader
AceReader(StanfordCoreNLP, boolean, String) AceReader
AceRelation edu.stanford.nlp.ie.machinereading.domains.ace.reader
Stores one ACE relation
AceRelation(String, String, String, String, String) AceRelation
AceRelationMention edu.stanford.nlp.ie.machinereading.domains.ace.reader
Stores one ACE relation mention
AceRelationMention(String, AceCharSeq, String) AceRelationMention
AceRelationMentionArgument edu.stanford.nlp.ie.machinereading.domains.ace.reader
AceRelationMentionArgument(String, AceEntityMention) AceRelationMentionArgument
AceSentenceSegmenter edu.stanford.nlp.ie.machinereading.domains.ace.reader
AceSentenceSegmenter() AceSentenceSegmenter
AceToken edu.stanford.nlp.ie.machinereading.domains.ace.reader
AceToken(String, String, String, String, String, String, String, int) AceToken
Constructs an AceToken from a tokenized line generated by Tokey
acos(double) SloppyMath
Compute acos very quickly by directly looking up the value.
ACRONYM RobustTokenizer
acronymCache Document
acronymCache Document
AcronymMatcher edu.stanford.nlp.util
A simple class with a variety of acronym matching utilities.
AcronymMatcher() AcronymMatcher
Action(Env) TokenSequenceParser
actionPerformed(ActionEvent) InputPanel
actionPerformed(ActionEvent) TregexGUI
actionPerformed(ActionEvent) TregexGUI.TransferActionListener
activate(MorphoFeatureSpecification.MorphoFeatureType) MorphoFeatureSpecification
active SequenceMatchRules.AnnotationExtractRule
activeFeatures MorphoFeatureSpecification
adaAlpha Config
Initial global learning rate for AdaGrad training
adaDeltaRho SeqClassifierFlags
adaEps Config
An epsilon value added to the denominator of the AdaGrad
expression for numerical stability
adaGrad(double, double) SimpleLinearClassifier
adaGradEps SeqClassifierFlags
adagradResetFrequency RNNTrainOptions
How many epochs between resets of the adagrad learning rates.
adapt(String, Dataset<String, String>, DocumentReaderAndWriter<IN>) CMMClassifier
adapt(ObjectBank<List<IN>>, Dataset<String, String>) CMMClassifier
adapt() JavaUtilLoggingAdaptor
AdaptedGaussianPriorObjectiveFunction L ,F > - Class in edu.stanford.nlp.classify
Adapt the mean of the Gaussian Prior by shifting the mean to the previously trained weights
AdaptedGaussianPriorObjectiveFunction(GeneralDataset<L, F>, LogPrior, double[][]) AdaptedGaussianPriorObjectiveFunction
adaptFile SeqClassifierFlags
NER adaptation (Gaussian prior) parameters.
adaptSigma SeqClassifierFlags
adaptWeights(Dataset<L, F>, LinearClassifierFactory<L, F>) LinearClassifier
adaptWeights(double[][], GeneralDataset<L, F>) LinearClassifierFactory
Adapt classifier (adjust the mean of Gaussian prior).
add(Datum<L, F>) Dataset
add(Collection<F>, L) Dataset
add(Collection<F>, L, boolean) Dataset
add(int[], int) Dataset
Adds a datums defined by feature indices and label index
Careful with this one! Make sure that all indices are valid!
add(Datum<L, F>) GeneralDataset
add(Datum<L, F>) RVFDataset
add(Datum<L, F>, String, String) RVFDataset
add(Datum<L, F>) WeightedDataset
add(Collection<F>, L) WeightedDataset
add(Datum<L, F>, float) WeightedDataset
add(Collection<F>, L, float) WeightedDataset
add(RVFDatum<L, F>, float) WeightedRVFDataset
Add a datum, with a given weight.
add(Datum<L, F>) WeightedRVFDataset add(Datum<L, F>, String, String) WeightedRVFDataset add(V, V, E) DirectedMultiGraph
adds vertices (if not already in the graph) and the edge between them
add(V, V, E) Graph
Adds vertices (if not already in the graph) and the edge between them.
add(K, NodePattern) ComplexNodePattern
add(String) PhraseTable.PhraseStringCollection
add(Expression) Expressions.ListExpression
add(double[], double) ArrayMath
add(float[], double) ArrayMath
add(Experiments) Experiments
add(Feature) Features
add(Feature) Problem
add(E) ObjectBank
Unsupported Operation.
add(Object) ReaderIteratorFactory
Adds an Object to the underlying Collection of input sources.
add(double, double[], double[], int, double) QNMinimizer.Record
add(Map<String, DataInstance>, boolean) InvertedIndexByTokens
add(List<CoreLabel>, String, boolean) InvertedIndexByTokens
add(Map<String, DataInstance>, boolean) SentenceIndex
addProcessedText is true when inserting sentences for the first time
add(List<CoreLabel>, String, boolean) SentenceIndex
add(List<IN>) ObjectBankWrapper
add(TwoDimensionalCounter<T1, T2>, TwoDimensionalCounter<T1, T2>) Counters
For all keys (u,v) in arg1 and arg2, sets return[u,v] to be summation of both.
add(Counter<E>, Collection<E>) Counters
add(Counter<E>, Counter<E>) Counters
add(Counter<E>, double) Counters
increments every key in the counter by value
add(FeatureKey) TaggerExperiments
Adds a FeatureKey to the set of known FeatureKeys.
add(int, History, int) TemplateHash
add(String) TTags
add(SUTime.Duration) JollyDayHolidays.JollyHoliday
add(SUTime.Duration) SUTime.Duration
add(SUTime.Duration) SUTime.DurationRange
add(SUTime.Duration) SUTime.DurationWithFields
add(SUTime.Duration) SUTime.DurationWithMillis
add(SUTime.Duration) SUTime.GroundedTime
add(SUTime.Duration) SUTime.InexactTime
add(SUTime.Duration) SUTime.OrdinalTime
add(SUTime.Duration) SUTime.PartialTime
add(SUTime.Duration) SUTime.Range
add(SUTime.Duration, int) SUTime.Range
add(SUTime.Duration) SUTime.RefTime
add(SUTime.Duration) SUTime.RelativeTime
add(SUTime.Duration) SUTime.SimpleTime
add(SUTime.Duration) SUTime.Time
add(SUTime.Duration) SUTime.TimeWithRange
add(int, Tree) MemoryTreebank
add(Tree) MemoryTreebank
add(E) ArrayHeap
Adds an object to the heap.
add(E) ArraySet
Adds element to set.
add(T) Beam
add(E) BinaryHeapPriorityQueue
Adds an object to the queue with the minimum priority
(Double.NEGATIVE_INFINITY).
add(E, double) BinaryHeapPriorityQueue
Convenience method for if you want to pretend relaxPriority doesn't exist,
or if you really want to use the return conditions of add().
add(K, V) CollectionValuedMap
Adds the value to the Collection mapped to by the key.
add(E) ConcurrentHashIndex
add(U, U) ConfusionMatrix
Increments the entry for this guess and gold by 1.
add(U, U, int) ConfusionMatrix
Increments the entry for this guess and gold by the given increment amount.
add(K, V) DeltaCollectionValuedMap
add(E) DeltaIndex
add(E, double) FixedPrioritiesPriorityQueue
Adds a key to the queue with the given priority.
add(E) HashIndex
Adds an object to the Index.
add(E) Heap
Adds the object to the heap.
add(E) IdentityHashSet
Adds the specified element to this set if it is not already present.
add(E) Index
add(T) IntervalTree
add(IntervalTree.TreeNode<E, T>, T) IntervalTree
add(IntervalTree.TreeNode<E, T>, T, double) IntervalTree
add(int) IntUni
add(U, V) MapList
add(U, Collection<V>) MapList
Using the iterator order of values in the value, adds the
individual elements into the list under the given key.
add(E) MaxSizeConcurrentHashSet
Adds the element if the set is not already full.
add(E, double) PriorityQueue
Convenience method for if you want to pretend relaxPriority doesn't exist,
or if you really want to use the return conditions of add().
add(K1, K2, V) TwoDimensionalCollectionValuedMap
Adds the value to the Collection mapped to by the key.
add(K1, K2, Collection<V>) TwoDimensionalCollectionValuedMap
Adds a collection of values to the Collection mapped to by the key.
add(K1, K2) TwoDimensionalSet
ADD_FUNCTION ValueFunctions
addAll(Iterable<? extends Datum<L, F>>) GeneralDataset
Adds all Datums in the given collection of data to this dataset
addAll(Iterable<? extends Datum<L, F>>) RVFDataset
addAll(Collection<? extends String>) PhraseTable.PhraseStringCollection
addAll(List<Expression>) Expressions.ListExpression
addAll(Collection<? extends E>) ObjectBank
Unsupported Operation.
addAll(Collection<?>) ReaderIteratorFactory
Adds all Objects in Collection c to the underlying Collection of
input sources.
addAll(List<TaggedWord>) BaseLexicon
Not yet implemented.
addAll(List<TaggedWord>, double) BaseLexicon
Not yet implemented.
addAll(Collection<? extends List<IN>>) ObjectBankWrapper
addAll(Counter<E>) AbstractCounter
Adds the counts in the given Counter to the counts in this Counter.
addAll(Counter<E>) ClassicCounter
Adds the counts in the given Counter to the counts in this Counter.
addAll(Counter<E>) Counter
Adds the counts in the given Counter to the counts in this Counter.
addAll(IntCounter<E>) IntCounter
Adds the counts in the given Counter to the counts in this Counter.
addAll(TwoDimensionalCounterInterface<K1, K2>) TwoDimensionalCounter
addAll(K1, Counter<K2>) TwoDimensionalCounter
addAll(TwoDimensionalIntCounter<K1, K2>) TwoDimensionalIntCounter
addAll(K1, IntCounter<K2>) TwoDimensionalIntCounter
addAll(int, Collection<? extends Tree>) MemoryTreebank
addAll(Collection<T>, Iterable<? extends T>) CollectionUtils
Add all the items from an iterable to a collection.
addAll(K, Collection<V>) CollectionValuedMap
Adds the values to the Collection mapped to by the key.
addAll(Map<K, V>) CollectionValuedMap
Adds all of the mappings in m to this CollectionValuedMap.
addAll(CollectionValuedMap<K, V>) CollectionValuedMap
addAll(Collection<? extends E>) ConcurrentHashIndex
addAll(Map<K, V>) DeltaCollectionValuedMap
Adds all of the mappings in m to this CollectionValuedMap.
addAll(Collection<? extends E>) DeltaIndex
addAll(Collection<? extends E>) HashIndex
Adds every member of Collection to the Index.
addAll(Collection<? extends E>) Index
addAll(Iterator<? extends T>, Collection<T>) Iterables
Adds all of the Objects returned by the given Iterator into the given Collection.
addAll(Map<K, V1>, Map<K, V2>, Function<V2, V1>) Maps
Adds all of the keys in from to to,
applying function to the values to transform them
from V2 to V1.
addAll(Collection<? extends E>) MaxSizeConcurrentHashSet
Add all the items.
addAll(TwoDimensionalMap<? extends K1, ? extends K2, ? extends V2>, Function<V2, V>) TwoDimensionalMap
Adds all of the entries in the other map, performing
function on them to transform the values
addAll(TwoDimensionalSet<? extends K1, ? extends K2>) TwoDimensionalSet
addAllAppositions(Iterable<? extends Integer>) CoreNLPProtos.Mention.Builder
repeated int32 appositions = 54;
addAllArg(Iterable<? extends CoreNLPProtos.Entity>) CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addAllArgName(Iterable<String>) CoreNLPProtos.Relation.Builder
repeated string argName = 6;
addAllBelongToLists(Iterable<? extends Integer>) CoreNLPProtos.Mention.Builder
repeated int32 belongToLists = 58;
addAllChild(Iterable<? extends CoreNLPProtos.ParseTree>) CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addAllCorefChain(Iterable<? extends CoreNLPProtos.CorefChain>) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addAllDependents(Iterable<String>) CoreNLPProtos.Mention.Builder
repeated string dependents = 52;
addAllEdge(Iterable<? extends CoreNLPProtos.DependencyGraph.Edge>) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addAllEntailedSentence(Iterable<? extends CoreNLPProtos.SentenceFragment>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addAllEntity(Iterable<? extends CoreNLPProtos.Entity>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addAllInterningAndSuffixing(Collection<String>, Collection<String>, String) FeatureFactory
Makes more complete feature names out of partial feature names, by
adding a suffix to the String feature name, adding results to an
accumulator
addAllKBestParseTrees(Iterable<? extends CoreNLPProtos.ParseTree>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addAllKey(Iterable<? extends Integer>) CoreNLPProtos.MapIntString.Builder
repeated uint32 key = 1;
addAllKey(Iterable<String>) CoreNLPProtos.MapStringString.Builder
repeated string key = 1;
addAllKeys(TwoDimensionalMap<? extends K1, ? extends K2, ?>) TwoDimensionalSet
Adds all the keys in the given TwoDimensionalMap.
addAllListMembers(Iterable<? extends Integer>) CoreNLPProtos.Mention.Builder
repeated int32 listMembers = 57;
addAllMacros(TregexPatternCompiler, String, String) Macros
addAllMacros(TregexPatternCompiler, BufferedReader) Macros
addAllMention(Iterable<? extends CoreNLPProtos.CorefChain.CorefMention>) CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addAllMentions(Iterable<? extends CoreNLPProtos.NERMention>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addAllMentions(Iterable<? extends Integer>) CoreNLPProtos.SpeakerInfo.Builder
repeated int32 mentions = 2;
addAllMentionsForCoref(Iterable<? extends CoreNLPProtos.Mention>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addAllNode(Iterable<? extends CoreNLPProtos.DependencyGraph.Node>) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addAllObjectTokens(Iterable<? extends Integer>) CoreNLPProtos.OpenIETriple.Builder
repeated uint32 objectTokens = 7;
addAllOpenieTriple(Iterable<? extends CoreNLPProtos.OpenIETriple>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addAllOriginalSpan(Iterable<? extends CoreNLPProtos.IndexedWord>) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addAllPredicateNominatives(Iterable<? extends Integer>) CoreNLPProtos.Mention.Builder
repeated int32 predicateNominatives = 55;
addAllPreprocessedTerms(Iterable<String>) CoreNLPProtos.Mention.Builder
repeated string preprocessedTerms = 53;
addAllQuote(Iterable<? extends CoreNLPProtos.Quote>) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addAllRelation(Iterable<? extends CoreNLPProtos.Relation>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addAllRelationTokens(Iterable<? extends Integer>) CoreNLPProtos.OpenIETriple.Builder
repeated uint32 relationTokens = 6;
addAllRelativePronouns(Iterable<? extends Integer>) CoreNLPProtos.Mention.Builder
repeated int32 relativePronouns = 56;
addAllRoot(Iterable<? extends Integer>) CoreNLPProtos.DependencyGraph.Builder
repeated uint32 root = 3 [packed = true];
addAllSentence(Iterable<? extends CoreNLPProtos.Sentence>) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addAllSentencelessToken(Iterable<? extends CoreNLPProtos.Token>) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addAllSentenceWords(Iterable<? extends CoreNLPProtos.IndexedWord>) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addAllSubjectTokens(Iterable<? extends Integer>) CoreNLPProtos.OpenIETriple.Builder
repeated uint32 subjectTokens = 5;
addAllToken(Iterable<? extends CoreNLPProtos.Token>) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addAllTokenIndex(Iterable<? extends Integer>) CoreNLPProtos.SentenceFragment.Builder
repeated uint32 tokenIndex = 1;
addAllValue(Iterable<String>) CoreNLPProtos.MapIntString.Builder
repeated string value = 2;
addAllValue(Iterable<String>) CoreNLPProtos.MapStringString.Builder
repeated string value = 2;
addAllWithSourcesAndIds(RVFDataset<L, F>) RVFDataset
addAllXmlContext(Iterable<String>) CoreNLPProtos.Token.Builder
repeated string xmlContext = 22;
addAnnotator(Annotator) AnnotationPipeline
addApposition(Mention) Mention
Find apposition
addApposition(Mention) Mention
Find apposition
addAppositions(int) CoreNLPProtos.Mention.Builder
repeated int32 appositions = 54;
addArc(Object, Object, Object, Object) TransducerGraph
addArc(TransducerGraph.Arc) TransducerGraph
addArc(int, int, String) Configuration
addArg(AceEntityMention, String) AceEventMention
addArg(ExtractionObject, String, boolean) EventMention
addArg(ExtractionObject) RelationMention
addArg(CoreNLPProtos.Entity) CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addArg(int, CoreNLPProtos.Entity) CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addArg(CoreNLPProtos.Entity.Builder) CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addArg(int, CoreNLPProtos.Entity.Builder) CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addArgBuilder() CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addArgBuilder(int) CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
addArgName(String) CoreNLPProtos.Relation.Builder
repeated string argName = 6;
addArgNameBytes(ByteString) CoreNLPProtos.Relation.Builder
repeated string argName = 6;
addArgs(List<ExtractionObject>, List<String>, boolean) EventMention
addBelongsToList(Mention) Mention
addBelongsToList(Mention) Mention
addBelongToLists(int) CoreNLPProtos.Mention.Builder
repeated int32 belongToLists = 58;
addBiasToEmbedding SeqClassifierFlags
addBinaryFeature(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent, CoreLabel, FeatureFactory.FeatureComponent) BasicFeatureFactory
addBinaryFeatures(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent, FeatureFactory.FeatureComponent, String, CoreLabel, FeatureFactory.FeatureComponent, FeatureFactory.FeatureComponent) BasicFeatureFactory
addBoundary() Lattice
addCapitalFeatures SeqClassifierFlags
addChild(CoreNLPProtos.ParseTree) CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addChild(int, CoreNLPProtos.ParseTree) CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addChild(CoreNLPProtos.ParseTree.Builder) CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addChild(int, CoreNLPProtos.ParseTree.Builder) CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addChild(int, Tree) Tree
Adds the tree t at the index position among the daughters.
addChild(Tree) Tree
Adds the tree t at the last index position among the daughters.
addChild(int, Tree) TreeGraphNode
Adds a child in the ith location.
addChild(LogRecordHandler) Redwood.RecordHandlerTree
addChildBuilder() CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addChildBuilder(int) CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
addChildTree(Redwood.RecordHandlerTree) Redwood.RecordHandlerTree
addComment(String) SemanticGraph
Store a comment line with this semantic graph.
addConstraint(ParserConstraint) Lattice
addCorefChain(CoreNLPProtos.CorefChain) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addCorefChain(int, CoreNLPProtos.CorefChain) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addCorefChain(CoreNLPProtos.CorefChain.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addCorefChain(int, CoreNLPProtos.CorefChain.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addCorefChainBuilder() CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addCorefChainBuilder(int) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
addCounts(PrecisionRecallStats) PrecisionRecallStats
Adds the counts from the given stats to the counts of this stats.
addDependencyPathFeatures(Counter<String>, RelationMention, EntityMention, EntityMention, List<String>, List<String>, Logger) BasicRelationFeatureFactory
addDependents(String) CoreNLPProtos.Mention.Builder
repeated string dependents = 52;
addDependentsBytes(ByteString) CoreNLPProtos.Mention.Builder
repeated string dependents = 52;
addDeterminer AbstractDataset
addDistsimFeatures(List<String>, CoreLabel, String) DistsimFeatureFactory
addEdge(LatticeEdge) Lattice
addEdge(CoreNLPProtos.DependencyGraph.Edge) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addEdge(int, CoreNLPProtos.DependencyGraph.Edge) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addEdge(CoreNLPProtos.DependencyGraph.Edge.Builder) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addEdge(int, CoreNLPProtos.DependencyGraph.Edge.Builder) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addEdge(IndexedWord, IndexedWord, GrammaticalRelation, double, boolean) SemanticGraph
addEdge(SemanticGraphEdge) SemanticGraph
addEdgeBuilder() CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addEdgeBuilder(int) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
addEdgeFeatures(List<String>, State, String, String, Tree, Tree) BasicFeatureFactory
Could potentially add the tags and words for the left and right
ends of the tree.
addEdgeFeatures2(List<String>, State, String, Tree) BasicFeatureFactory
This option also does not seem to help
addEntailedSentence(CoreNLPProtos.SentenceFragment) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addEntailedSentence(int, CoreNLPProtos.SentenceFragment) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addEntailedSentence(CoreNLPProtos.SentenceFragment.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addEntailedSentence(int, CoreNLPProtos.SentenceFragment.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addEntailedSentenceBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addEntailedSentenceBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
addEntity(AceEntity) AceDocument
addEntity(CoreNLPProtos.Entity) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addEntity(int, CoreNLPProtos.Entity) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addEntity(CoreNLPProtos.Entity.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addEntity(int, CoreNLPProtos.Entity.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addEntityBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addEntityBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
addEntityMention(AceEntityMention) AceDocument
addEntityMention(CoreMap, EntityMention) AnnotationUtils
addEntityMentions(CoreMap, Collection<EntityMention>) AnnotationUtils
addEvent(AceEvent) AceDocument
addEventMention(AceEventMention) AceDocument
addEventMention(AceEventMention) AceEntityMention
addEventMention(CoreMap, EventMention) AnnotationUtils
addEventMentions(CoreMap, Collection<EventMention>) AnnotationUtils
addExample(List<Integer>, List<Integer>) Dataset
addExtraTrigramFeatures(List<String>, CoreLabel, CoreLabel, CoreLabel, CoreLabel, CoreLabel) BasicFeatureFactory
Also did not seem to help
addFeature(MorphoFeatureSpecification.MorphoFeatureType, String) MorphoFeatures
addFeature(String) TregexPoweredTreebankParserParams
Enable an annotation feature.
addFeature(String, double) CandidatePhrase
addFeatureIndices(int[]) Dataset
addFeatures(Collection<F>) Dataset
addFeatures(Collection<F>, boolean) Dataset
addFeatures(Counter<String>, RelationMention, List<String>) BasicRelationFeatureFactory
addFeatures(Counter<String>, RelationMention, List<String>, Logger) BasicRelationFeatureFactory
Creates all features for the datum corresponding to this relation mention
Note: this assumes binary relations where both arguments are EntityMention
addFeatures(Collection<String>) CandidatePhrase
addFeatures(SemanticGraph, Tree, boolean, boolean) UniversalDependenciesFeatureAnnotator
addFileFolder(EnumMap<TregexGUI.FilterType, String>, File[]) FileTreeModel
Forks off a new thread to load your files based on the filters you set in the interface
addFiles(List<String>) DistributionPackage
Adds a listing of files to the distribution archive
addFilter(Predicate<T>) Filters.ConjFilter
addFilter(Predicate<T>) Filters.DisjFilter
addFN(int) PrecisionRecallStats
Adds the given number to the count of false negatives.
addForce(Partial, Period, int) JodaTimeUtils
addForm(String) PhraseTable.Phrase
addFP(int) PrecisionRecallStats
Adds the given number to the count of false positives.
addGoldMentions(List<CoreMap>, List<Set<IntPair>>, List<List<Mention>>, List<List<Mention>>) CorefMentionFinder
addGuess(String, String, boolean) MultiClassChunkEvalStats
addGuess(LabeledChunkIdentifier.LabelTagType, LabeledChunkIdentifier.LabelTagType, boolean) MultiClassChunkEvalStats
addGuess(L, L) MultiClassPrecisionRecallExtendedStats
addGuess(L, L, boolean) MultiClassPrecisionRecallExtendedStats
addGuesses(List<L>, List<L>) MultiClassPrecisionRecallExtendedStats
addHcorefProps(Properties) StatisticalCorefProperties
addHighlight(JTextField, MouseEvent, MouseEvent) HighlightUtils
Highlight the given label from the first mouse event to the second
Returns true if the highlight was successful, false otherwise.
addIncompatible(Mention, Mention) Document
addIncompatible(Mention, Mention) Document
addIndvWordsFromPhrasesExceptLastAsNeg ConstantsAndVariables
For example, if positive seed dict contains "cancer" and "breast cancer" then "breast" is included as negative
addInPlace(double[], double) ArrayMath
Increases the values in this array by b.
addInPlace(float[], double) ArrayMath
Increases the values in this array by b.
addInPlace(Counter<E>, Counter<E>, double) Counters
Sets each value of target to be target[k]+scale*arg[k] for all keys k in
target.
addInPlace(Counter<E>, Counter<E>) Counters
Sets each value of target to be target[k]+arg[k] for all keys k in arg.
addInPlace(double[], Counter<E>, Index<E>) Counters
Sets each value of double[] target to be
target[idx.indexOf(k)]+a.getCount(k) for all keys k in arg
addInPlace(TwoDimensionalCounter<T1, T2>, TwoDimensionalCounter<T1, T2>, double) Counters
For all keys (u,v) in arg, sets target[u,v] to be target[u,v] + scale *
arg[u,v].
addInPlace(TwoDimensionalCounter<T1, T2>, TwoDimensionalCounter<T1, T2>) Counters
For all keys (u,v) in arg, sets target[u,v] to be target[u,v] + arg[u,v].
addInPlace(Counter<E>, Collection<E>, double) Counters
Sets each value of target to be target[k]+
value*(num-of-times-it-occurs-in-collection) if the key is present in the arg
collection.
addInPlace(TwoDimensionalCounter<T1, T2>, double) Counters
For all keys (u,v) in target, sets target[u,v] to be target[u,v] + value
addInPlace(Counter<E>, Collection<E>) Counters
Sets each value of target to be target[k]+
num-of-times-it-occurs-in-collection if the key is present in the arg
collection.
addInPlace(Counter<E>, double) Counters
Increments all keys in a Counter by a specific value.
addIntervalRelationFlags(int, boolean) Interval
additionalSignature() AnnotatorFactory
addKBestParseTrees(CoreNLPProtos.ParseTree) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addKBestParseTrees(int, CoreNLPProtos.ParseTree) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addKBestParseTrees(CoreNLPProtos.ParseTree.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addKBestParseTrees(int, CoreNLPProtos.ParseTree.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addKBestParseTreesBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addKBestParseTreesBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree kBestParseTrees = 34;
addKey(int) CoreNLPProtos.MapIntString.Builder
repeated uint32 key = 1;
addKey(String) CoreNLPProtos.MapStringString.Builder
repeated string key = 1;
addKey(K) CollectionValuedMap
addKey(K1) TwoDimensionalCollectionValuedMap
yes, this is a weird method, but i need it.
addKeyBytes(ByteString) CoreNLPProtos.MapStringString.Builder
repeated string key = 1;
addLabel(L) Dataset
addLabel(LabelType) BasicDatum
Adds the given Label to the List of labels for this Datum if it is not
null.
addLabel(L) BasicDocument
Adds the given label to the List of labels for this Document if it is not null.
addLabelIndex(int) Dataset
addListener(FileTreeNode.FileTreeNodeListener) FileTreeNode
addListener(MatchesPanel.MatchesPanelListener) MatchesPanel
Become a listener to changes in the trees the matches panel is showing
addListener(ListSelectionListener) MatchesPanel
Become a listener to changes in which tree is selected
addListMember(Mention) Mention
addListMember(Mention) Mention
addListMembers(int) CoreNLPProtos.Mention.Builder
repeated int32 listMembers = 57;
addMacro(String, String) TregexPatternCompiler
Define a macro for rewriting a pattern in any tregex expression compiled
by this compiler.
addMap(Map<String, Set<String>>) ChineseEnglishWordMap
Add all of the mappings from the specified map to the current map.
addMention(Mention) SpeakerInfo
addMention(Mention) SpeakerInfo
addMention(AceEntityMention) AceEntity
addMention(AceEventMention) AceEvent
addMention(AceRelationMention) AceRelation
addMention(CoreNLPProtos.CorefChain.CorefMention) CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addMention(int, CoreNLPProtos.CorefChain.CorefMention) CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addMention(CoreNLPProtos.CorefChain.CorefMention.Builder) CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addMention(int, CoreNLPProtos.CorefChain.CorefMention.Builder) CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addMentionBuilder() CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addMentionBuilder(int) CoreNLPProtos.CorefChain.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain.CorefMention mention = 2;
addMentions(CoreNLPProtos.NERMention) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addMentions(int, CoreNLPProtos.NERMention) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addMentions(CoreNLPProtos.NERMention.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addMentions(int, CoreNLPProtos.NERMention.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addMentions(int) CoreNLPProtos.SpeakerInfo.Builder
repeated int32 mentions = 2;
addMentionsBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addMentionsBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.NERMention mentions = 55;
addMentionsForCoref(CoreNLPProtos.Mention) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addMentionsForCoref(int, CoreNLPProtos.Mention) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addMentionsForCoref(CoreNLPProtos.Mention.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addMentionsForCoref(int, CoreNLPProtos.Mention.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addMentionsForCorefBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addMentionsForCorefBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Mention mentionsForCoref = 56;
addMissingAnnotations(Properties) CorefProperties
addMissingFinalPunctuation TestOptions
If a token list does not have sentence final punctuation near the
end, then automatically add the default one.
addMod(String) SUTime.Temporal
addMod() TimeExpression
addModApprox(String, boolean) SUTime.Temporal
addMultInPlace(double[], double[], double) ArrayMath
Add c times the array b to array a.
addMultInto(double[], double[], double[], double) ArrayMath
addNamedEntityStrings(CoreMap, Set<String>, Set<IntPair>) CorefMentionFinder
addNode(CoreNLPProtos.DependencyGraph.Node) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addNode(int, CoreNLPProtos.DependencyGraph.Node) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addNode(CoreNLPProtos.DependencyGraph.Node.Builder) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addNode(int, CoreNLPProtos.DependencyGraph.Node.Builder) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addNodeBuilder() CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addNodeBuilder(int) CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Node node = 1;
addNonNested(T) IntervalTree
addNonOverlapping(T) IntervalTree
addNormalizedQuantitiesToEntities(List<E>) QuantifiableEntityNormalizer
Identifies contiguous MONEY, TIME, DATE, or PERCENT entities
and tags each of their constituents with a "normalizedQuantity"
label which contains the appropriate normalized string corresponding to
the full quantity.
addNormalizedQuantitiesToEntities(List<E>, boolean) QuantifiableEntityNormalizer
addNormalizedQuantitiesToEntities(List<E>, boolean, boolean) QuantifiableEntityNormalizer
Identifies contiguous MONEY, TIME, DATE, or PERCENT entities
and tags each of their constituents with a "normalizedQuantity"
label which contains the appropriate normalized string corresponding to
the full quantity.
addObjectTokens(int) CoreNLPProtos.OpenIETriple.Builder
repeated uint32 objectTokens = 7;
addOneBinaryRule(BinaryRule, Map<String, TransducerGraph>) GrammarCompactor
addOnePathToGraph(List, double, int, TransducerGraph) TransducerGraph
addOneUnaryRule(UnaryRule, Map<String, TransducerGraph>) GrammarCompactor
addOpenieTriple(CoreNLPProtos.OpenIETriple) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addOpenieTriple(int, CoreNLPProtos.OpenIETriple) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addOpenieTriple(CoreNLPProtos.OpenIETriple.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addOpenieTriple(int, CoreNLPProtos.OpenIETriple.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addOpenieTripleBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addOpenieTripleBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.OpenIETriple openieTriple = 14;
addOriginalSpan(CoreNLPProtos.IndexedWord) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addOriginalSpan(int, CoreNLPProtos.IndexedWord) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addOriginalSpan(CoreNLPProtos.IndexedWord.Builder) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addOriginalSpan(int, CoreNLPProtos.IndexedWord.Builder) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addOriginalSpanBuilder() CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addOriginalSpanBuilder(int) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord originalSpan = 51;
addORRestriction(Class, String) Token
addParent(EventMention) EventMention
addPatterns(Map<String, Map<Integer, Set<E>>>) PatternsForEachToken
addPatterns(String, Map<Integer, Set<E>>) PatternsForEachToken
addPatterns(Map<String, Map<Integer, Set<E>>>) PatternsForEachTokenDB
addPatterns(String, Map<Integer, Set<E>>) PatternsForEachTokenDB
addPatterns(String, Map<Integer, Set<E>>) PatternsForEachTokenInMemory
addPatterns(Map<String, Map<Integer, Set<E>>>) PatternsForEachTokenInMemory
addPatWithoutPOS SurfacePatternFactory
Add patterns without POS restriction as well: One of this and
usePOS4Pattern has to be true.
addPhrase(String) PhraseTable
addPhrase(String, String) PhraseTable
addPhrase(String, String, Object) PhraseTable
addPhrase(List<String>) PhraseTable
addPhrase(List<String>, String) PhraseTable
addPhrase(List<String>, String, Object) PhraseTable
addPhrases(Collection<String>) PhraseTable
addPhrases(Map<String, String>) PhraseTable
addPositionFeatures(List<String>, State) BasicFeatureFactory
addPositions(int, int, FeatureKey) TemplateHash
addPredicateNominatives(Mention) Mention
Find predicate nominatives
addPredicateNominatives(Mention) Mention
Find predicate nominatives
addPredicateNominatives(int) CoreNLPProtos.Mention.Builder
repeated int32 predicateNominatives = 55;
addPreprocessedTerms(String) CoreNLPProtos.Mention.Builder
repeated string preprocessedTerms = 53;
addPreprocessedTermsBytes(ByteString) CoreNLPProtos.Mention.Builder
repeated string preprocessedTerms = 53;
addPrev(int, History) TemplateHash
addProcessedData(List<List<CRFDatum<Collection<String>, String>>>, int[][][][], int[][], double[][][][], int) CRFClassifier
Adds the List of Lists of CRFDatums to the data and labels arrays, treating
each datum as if it were its own document.
addQuote(CoreNLPProtos.Quote) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addQuote(int, CoreNLPProtos.Quote) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addQuote(CoreNLPProtos.Quote.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addQuote(int, CoreNLPProtos.Quote.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addQuoteBuilder() CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addQuoteBuilder(int) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Quote quote = 6;
addRandomBinaryMatrix(String, String) DVModel
addRandomColors OutputHandler
addRandomUnaryMatrix(String) DVModel
addRelation(AceRelation) AceDocument
addRelation(CoreNLPProtos.Relation) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addRelation(int, CoreNLPProtos.Relation) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addRelation(CoreNLPProtos.Relation.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addRelation(int, CoreNLPProtos.Relation.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addRelationBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addRelationBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Relation relation = 53;
addRelationMention(AceRelationMention) AceDocument
addRelationMention(AceRelationMention) AceEntityMention
addRelationMention(CoreMap, RelationMention) AnnotationUtils
addRelationMentions(CoreMap, Collection<RelationMention>) AnnotationUtils
addRelationTokens(int) CoreNLPProtos.OpenIETriple.Builder
repeated uint32 relationTokens = 6;
addRelativePronoun(Mention) Mention
Find relative pronouns
addRelativePronoun(Mention) Mention
Find relative pronouns
addRelativePronouns(int) CoreNLPProtos.Mention.Builder
repeated int32 relativePronouns = 56;
addRelnToNodeCoord(SemgrexPattern) CoordinationPattern
addRoot(Tree) TreeAnnotatorAndBinarizer
Changes the ROOT label, and adds a Lexicon.BOUNDARY daughter to it.
addRoot(int) CoreNLPProtos.DependencyGraph.Builder
repeated uint32 root = 3 [packed = true];
addRoot(IndexedWord) SemanticGraph
addRoot AbstractDataset
addRule(BinaryRule) BinaryGrammar
addRule(IntDependency, double) MLEDependencyGrammar
Add this dependency with the given count to the grammar.
addRule(UnaryRule) UnaryGrammar
addRules(SequenceMatchRules.ExtractRule<I, O>...) SequenceMatchRules.ListExtractRule
addRules(Collection<SequenceMatchRules.ExtractRule<I, O>>) SequenceMatchRules.ListExtractRule
addScaled(Weight, float) Weight
addSeedWords(String, Collection<CandidatePhrase>) ConstantsAndVariables
addSentence(List<String[]>) CoNLL2011DocumentReader.Document
addSentence(List<String[]>) CoNLLDocumentReader.CoNLLDocument
addSentence(CoreMap, CoreMap) AnnotationUtils
addSentence(CoreNLPProtos.Sentence) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addSentence(int, CoreNLPProtos.Sentence) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addSentence(CoreNLPProtos.Sentence.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addSentence(int, CoreNLPProtos.Sentence.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addSentenceBuilder() CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addSentenceBuilder(int) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Sentence sentence = 2;
addSentencelessToken(CoreNLPProtos.Token) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addSentencelessToken(int, CoreNLPProtos.Token) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addSentencelessToken(CoreNLPProtos.Token.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addSentencelessToken(int, CoreNLPProtos.Token.Builder) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addSentencelessTokenBuilder() CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addSentencelessTokenBuilder(int) CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.Token sentencelessToken = 5;
addSentences(CoreMap, List<CoreMap>) AnnotationUtils
addSentenceWords(CoreNLPProtos.IndexedWord) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addSentenceWords(int, CoreNLPProtos.IndexedWord) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addSentenceWords(CoreNLPProtos.IndexedWord.Builder) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addSentenceWords(int, CoreNLPProtos.IndexedWord.Builder) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addSentenceWordsBuilder() CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addSentenceWordsBuilder(int) CoreNLPProtos.Mention.Builder
repeated .edu.stanford.nlp.pipeline.IndexedWord sentenceWords = 50;
addSeparatorFeature(List<String>, String, State.HeadPosition) BasicFeatureFactory
addSeparatorFeature(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent, State.HeadPosition) BasicFeatureFactory
addSeparatorFeature(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent, boolean) BasicFeatureFactory
addSeparatorFeature(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent, CoreLabel, FeatureFactory.FeatureComponent, boolean) BasicFeatureFactory
addSeparatorFeatures(List<String>, String, CoreLabel, String, CoreLabel, String, int) BasicFeatureFactory
addSeparatorFeatures(List<String>, CoreLabel, CoreLabel, State.HeadPosition, State.HeadPosition) BasicFeatureFactory
addSplit(FastExactAutomatonMinimizer.Split) FastExactAutomatonMinimizer
addSplits(FastExactAutomatonMinimizer.Block) FastExactAutomatonMinimizer
addSRLArcs SemanticGraph
addSubjectTokens(int) CoreNLPProtos.OpenIETriple.Builder
repeated uint32 subjectTokens = 5;
addSupported(Period, int) SUTime.CompositePartialTime
addSupported(Period, int) SUTime.PartialTime
addTag(String, Value) Tags
addTag(String) MaxentTagger
Will return the index of a tag, adding it if it doesn't already exist
addTagging(boolean, IntTaggedWord, double) BaseLexicon
Adds the tagging with count to the data structures in this Lexicon.
addTagging(boolean, IntTaggedWord, double) BaseUnknownWordModel
Adds the tagging with count to the data structures in this Lexicon.
addTagging(boolean, IntTaggedWord, double) UnknownWordModel
Adds the tagging with count to the data structures in this Lexicon.
addTemporal(SUTime.Temporal) SUTime.TimeIndex
addTemporalExpr(TimeExpression) SUTime.TimeIndex
addTemporalFunc(SUTime.Temporal) SUTime.TimeIndex
addToIndex(E) ConcurrentHashIndex
addToIndex(E) DeltaIndex
addToIndex(E) HashIndex
addToIndex(E) Index
Takes an Object and returns the integer index of the Object.
addToIndexTemporal(SUTime.Temporal) SUTime.TimeIndex
addToIndexTemporalExpr(TimeExpression) SUTime.TimeIndex
addToIndexTemporalFunc(SUTime.Temporal) SUTime.TimeIndex
addToIndexUnsafe(E) HashIndex
Add the given item to the index, but without taking any locks.
addToken(AceToken) AceDocument
addToken(CoreNLPProtos.Token) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addToken(int, CoreNLPProtos.Token) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addToken(CoreNLPProtos.Token.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addToken(int, CoreNLPProtos.Token.Builder) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addTokenBuilder() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addTokenBuilder(int) CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Token token = 1;
addTokenIndex(int) CoreNLPProtos.SentenceFragment.Builder
repeated uint32 tokenIndex = 1;
addToKeySet(E) Distribution
Insures that object is in keyset (with possibly zero value)
addTP(int) PrecisionRecallStats
Adds the given number to the count of true positives.
addTransformer(TreeTransformer) CompositeTreeTransformer
addTransition(DFSATransition<T, S>) DFSAState
addTreeModelListener(TreeModelListener) FileTreeModel
addTreeNormalizer(TreeNormalizer) OrderedCombinationTreeNormalizer
Adds the given tree normalizer to this combination; the tree normalizers
are applied in the order they were added, with the first to be added being
the first to be applied.
addTrigramFeature(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent, CoreLabel, FeatureFactory.FeatureComponent, CoreLabel, FeatureFactory.FeatureComponent) BasicFeatureFactory
addUnaryFeature(List<String>, String, CoreLabel, FeatureFactory.FeatureComponent) BasicFeatureFactory
addUnaryQueueFeatures(List<String>, CoreLabel, String) BasicFeatureFactory
addUnaryStackFeatures(List<String>, CoreLabel, String, String, String, String, String) BasicFeatureFactory
addUnsupported(Period, int) SUTime.CompositePartialTime
addUnsupported(Period, int) SUTime.PartialTime
addValue(String) CoreNLPProtos.MapIntString.Builder
repeated string value = 2;
addValue(String) CoreNLPProtos.MapStringString.Builder
repeated string value = 2;
addValueBytes(ByteString) CoreNLPProtos.MapIntString.Builder
repeated string value = 2;
addValueBytes(ByteString) CoreNLPProtos.MapStringString.Builder
repeated string value = 2;
addVertex(V) DirectedMultiGraph
For adding a zero degree vertex
addVertex(V) Graph
For adding a zero degree vertex
addVertex(IndexedWord) SemanticGraph
addVThatTaking(String) Dictionary
addWordShapes(String, Set<CandidatePhrase>) ConstantsAndVariables
addXmlContext(String) CoreNLPProtos.Token.Builder
repeated string xmlContext = 22;
addXmlContextBytes(ByteString) CoreNLPProtos.Token.Builder
repeated string xmlContext = 22;
ADJECTIVAL_COMPLEMENT EnglishGrammaticalRelations
The "adjectival complement" grammatical relation.
ADJECTIVAL_MODIFIER EnglishGrammaticalRelations
The "adjectival modifier" grammatical relation.
ADJECTIVAL_MODIFIER ChineseGrammaticalRelations
The "adjective modifier" (amod) grammatical relation.
ADJECTIVAL_MODIFIER UniversalEnglishGrammaticalRelations
The "adjectival modifier" grammatical relation.
adjSuffix(String) FrenchUnknownWordSignatures
adjustBias(List<List<IN>>, Function<Double, Double>, double, double) CRFBiasedClassifier
Adjust the bias parameter to optimize some objective function.
adjustPhrasePositions(int, String) AceToken
Recomputes start/end phrase positions by removing SGML tag strings This is
required because ACE annotations skip over SGML tags when computing
positions in stream, hence annotations do not match with our preprocessing
positions, which count everything
ADMath edu.stanford.nlp.math
The class ADMath was created to extend the
current calculations of gradient to automatically include a calculation of the
hessian vector product with another vector v.
ADV_CLAUSE_MODIFIER EnglishGrammaticalRelations
The "adverbial clause modifier" grammatical relation.
ADV_CLAUSE_MODIFIER UniversalEnglishGrammaticalRelations
The "adverbial clause modifier" grammatical relation.
advanceLeft(boolean[]) OutsideRuleFilter
advanceRight(boolean[]) OutsideRuleFilter
ADVERBIAL_MODIFIER EnglishGrammaticalRelations
The "adverbial modifier" grammatical relation.
ADVERBIAL_MODIFIER ChineseGrammaticalRelations
The "adverbial modifier" (advmod) grammatical relation.
ADVERBIAL_MODIFIER UniversalEnglishGrammaticalRelations
The "adverbial modifier" grammatical relation.
adverbSuffix(String) SpanishUnknownWordSignatures
advSuffix(String) FrenchUnknownWordSignatures
affDict edu.stanford.nlp.wordseg
affixation information
affDict(String) affDict
after(AceEntityMention) AceEntityMention
Verifies if this mention appears after the parameter in textual order
after() CoreLabel
Return the whitespace String after the word.
after() HasContext
Return the whitespace String after the word.
AFTER_FIELD_NUMBER CoreNLPProtos.Token
AfterAnnotation() CoreAnnotations.AfterAnnotation
AFTERNOON SUTime
agenda BiLexPCFGParser
AGENT EnglishGrammaticalRelations
The "agent" grammatical relation.
AGENT UniversalEnglishGrammaticalRelations
The "agent" grammatical relation.
aggregate(Class, List<? extends CoreMap>) CoreMapAttributeAggregator
aggregate(Class, List<? extends CoreMap>) CoreMapAttributeAggregator.ConcatAggregator
aggregate(Class, List<? extends CoreMap>) CoreMapAttributeAggregator.ConcatCoreMapListAggregator
aggregate(Class, List<? extends CoreMap>) CoreMapAttributeAggregator.ConcatListAggregator
aggregate(Class, List<? extends CoreMap>) CoreMapAttributeAggregator.ConcatTextAggregator
aggregate(Class, List<? extends CoreMap>) CoreMapAttributeAggregator.MostFreqAggregator
AGGREGATE_FUNCTION ValueFunctions
aggregateToTokens Env
Whether we should merge and output corelabels or not
algorithms() Sentence
Return a class that can perform common algorithms on this sentence.
AliasMatch edu.stanford.nlp.dcoref.sievepasses
AliasMatch() AliasMatch
AliasMatch edu.stanford.nlp.hcoref.sieve
AliasMatch() AliasMatch
align(List<CoreMap>, List<CoreMap>) ResultsPrinter
If the same set of sentences is contained in two lists, order the lists so that their sentences are in the same order (and return true).
alignDDAlpha SeqClassifierFlags
alignLeft(JComponent) PreferencesPanel
Alignment edu.stanford.nlp.semgraph.semgrex
Represents an alignment between a text and a hypothesis as a map from
hypothesis words to text words, along with a real-valued score and
(optionally) a justification string.
Alignment(Map<IndexedWord, IndexedWord>, double, String) Alignment
alignmentDecodeThreshold SeqClassifierFlags
alignmentPruneThreshold SeqClassifierFlags
alignSentence(CoreMap) NumberSequenceClassifier
Copies one sentence replicating only information necessary for SUTime
allCandidatePhrases ScorePatterns
allEdgesInSet(Iterable<IndexedWord>, SemanticGraph) SemanticGraphUtils
Given a set of vertices from the same graph, returns the set of all edges between these
vertices.
allGoldMentions Document
allIndices AbstractStochasticCachingDiffFunction
allLabels(int, Index<String>) CRFClassifier
ALLOW_EMBEDDED_SAME QuoteAnnotator
ALLOW_REPARSING Constants
Whether or not the RuleBasedCorefMentionFinder can reparse a phrase to find its head
ALLOW_REPARSING_PROP Constants
ALLOW_REPARSING_PROP CorefProperties
allowedNERsforLabels ConstantsAndVariables
allowedTagsInitials ConstantsAndVariables
allowReparsing(Properties) CorefProperties
allowReparsing CorefMentionFinder
allPatternsDir ConstantsAndVariables
Cached file of all patterns for all tokens
allPositions Document
allPositions Document
allPredictedMentions Document
All mentions in a document mentionID -> mention
allPronouns Dictionaries
allPronouns Dictionaries
AllRelationMentionsAnnotation() MachineReadingAnnotations.AllRelationMentionsAnnotation
allSpans(Function<Sentence, List<E>>, int) SentenceAlgorithms
Return all the spans of a sentence.
allSpans(Function<Sentence, List<E>>) SentenceAlgorithms
allSpans() SentenceAlgorithms
allTokens() RelationTriple
Returns all the tokens in the extraction, in the order subject then relation then object.
allTypedDependencies(Tree, boolean) SemanticGraphFactory
Returns a new SemanticGraph constructed from the given tree.
allTypedDependencies GrammaticalStructure
allTypedDependencies() GrammaticalStructure
Returns all the typed dependencies of this grammatical structure.
allValues() CollectionValuedMap
alpha SGDWithAdaGradAndFOBOS
alphaFile SVMLightClassifierFactory
alsoHide(Object) VisibilityHandler
Show all the channels currently being printed, with the exception
of this new one
alsoShow(Object) VisibilityHandler
Show all the channels currently being printed, in addition
to a new one
altAnswerFile SeqClassifierFlags
alternateTraining SeqClassifierFlags
ALTERNATION_VALUE CoreNLPProtos.NaturalLogicRelation
ALTERNATION = 4;
ALTERNATIVEDEPENDENCIES_FIELD_NUMBER CoreNLPProtos.Sentence
AlternativeDependenciesAnnotation() SemanticGraphCoreAnnotations.AlternativeDependenciesAnnotation
altTag MorphoFeatures
altVal() Timex
ALTVALUE_FIELD_NUMBER CoreNLPProtos.Timex
am HTKLatticeReader.LatticeWord
AmbiguityClass edu.stanford.nlp.tagger.maxent
An ambiguity class for a word is the word by itself or its set of observed tags.
AmbiguityClass(String, boolean, Dictionary, TTags) AmbiguityClass
AmbiguityClasses edu.stanford.nlp.tagger.maxent
A collection of Ambiguity Class.
AmbiguityClasses(TTags) AmbiguityClasses
Americanize edu.stanford.nlp.process
Takes a HasWord or String and returns an Americanized version of it.
Americanize() Americanize
Americanize(int) Americanize
Make an object for Americanizing spelling.
americanize(String) Americanize
Convert the spelling of a word from British to American English.
americanize(String, boolean) Americanize
Convert the spelling of a word from British to American English.
AmericanizeFunction edu.stanford.nlp.process
AmericanizeFunction() AmericanizeFunction
AMPERSAND RobustTokenizer
ANALYSIS_SKIP_ATYPE_PROP CorefProperties
ANALYSIS_SKIP_MTYPE_PROP CorefProperties
ANAPHORICITY_MODEL StatisticalCorefTrainer
anaphoricityClassifier(boolean) MetaFeatureExtractor.Builder
anaphoricityMFE() MetaFeatureExtractor
anaphoricityModelPath(Properties) StatisticalCorefProperties
anaphoricityScores ClustererDataLoader.ClustererDoc
ancestor(int, Tree) Tree
Return the ancestor tree node height nodes up from the current node.
ANCORA_OPTIONS SpanishTokenizer
ancoraFactory() SpanishTokenizer
Returns a tokenizer with Ancora tokenization.
AnCoraPronounDisambiguator edu.stanford.nlp.international.spanish.process
A utility for preprocessing the AnCora Spanish corpus.
AnCoraPronounDisambiguator() AnCoraPronounDisambiguator
AnCoraPronounDisambiguator.PersonalPronounType edu.stanford.nlp.international.spanish.process
and(Trilean) Trilean
Returns the logical and of this and the other value.
AND_FUNCTION ValueFunctions
AndExpression(List<Expression>) Expressions.AndExpression
andFilter(Predicate<E>, Predicate<E>) Filters
Filter that accepts only when both filters accept (AND).
AndPatternExpr(List<SequencePattern.PatternExpr>) SequencePattern.AndPatternExpr
AndPatternExpr(SequencePattern.PatternExpr...) SequencePattern.AndPatternExpr
animacies CorefCluster
animacies CorefCluster
animaciesAgree(Mention) Mention
animaciesAgree(Mention, boolean) Mention
animaciesAgree(Mention) Mention
animaciesAgree(Mention, boolean) Mention
animacy CorefChain.CorefMention
animacy Mention
animacy CorefChain.CorefMention
animacy Mention
ANIMACY_FIELD_NUMBER CoreNLPProtos.CorefChain.CorefMention
ANIMACY_FIELD_NUMBER CoreNLPProtos.Mention
ANIMATE_PROP Constants
ANIMATE_PROP CorefProperties
animatePronouns Dictionaries
animatePronouns Dictionaries
animatePronounsEn WordLists
animatePronounsZh WordLists
animateWords Dictionaries
animateWords Dictionaries
annealingRate SeqClassifierFlags
annealingType SeqClassifierFlags
annotate(Annotation) BasicEntityExtractor
Annotate an ExtractionDataSet with entities.
annotate(Annotation) BasicRelationExtractor
annotate(Annotation) Extractor
Annotates the given dataset with the current model This works in place,
i.e., it adds ExtractionObject objects to the sentences in the dataset To
make sure you are not messing with gold annotation create a copy of the
ExtractionDataSet first!
annotate(Annotation) ExtractorMerger
annotate(Annotation) MachineReading
annotate(Annotation, int) MachineReading
annotate(MatchedExpression, List<? extends CoreMap>) MatchedExpression.SingleAnnotationExtractor
annotate(MatchedExpression) MatchedExpression.SingleAnnotationExtractor
annotate(Annotation) OpenIE
Given an Annotation, perform a task on this Annotation.
annotate(CoreMap, AnnotationPipeline) Util
TODO(gabor) JavaDoc
annotate(Annotation) AnnotationPipeline
Run the pipeline on an input annotation.
annotate(Iterable<Annotation>) AnnotationPipeline
Annotate a collection of input annotations IN PARALLEL, making use of
all available cores.
annotate(Iterable<Annotation>, Consumer<Annotation>) AnnotationPipeline
Annotate a collection of input annotations IN PARALLEL, making use of
all available cores.
annotate(Iterable<Annotation>, int) AnnotationPipeline
Annotate a collection of input annotations IN PARALLEL, making use of
threads given in numThreads.
annotate(Iterable<Annotation>, int, Consumer<Annotation>) AnnotationPipeline
Annotate a collection of input annotations IN PARALLEL, making use of
threads given in numThreads
annotate(Annotation) Annotator
Given an Annotation, perform a task on this Annotation.
annotate(Annotation) BinarizerAnnotator
annotate(Annotation) CharniakParserAnnotator
annotate(Annotation) ChineseSegmenterAnnotator
annotate(Annotation) CleanXmlAnnotator
annotate(Annotation) ColumnDataClassifierAnnotator
annotate(Annotation) CorefAnnotator
annotate(Annotation) DeterministicCorefAnnotator
annotate(Annotation) EntityMentionsAnnotator
annotate(Annotation) GenderAnnotator
annotate(Annotation) MentionAnnotator
annotate(Annotation) MorphaAnnotator
annotate(Annotation) NERCombinerAnnotator
annotate(Annotation) POSTaggerAnnotator
annotate(Annotation) QuoteAnnotator
annotate(Annotation) RegexNERAnnotator
annotate(Annotation) RelationExtractorAnnotator
annotate(Annotation) SentenceAnnotator
annotate(Annotation) SentimentAnnotator
annotate(Annotation) StanfordCoreNLP
annotate(Annotation, Consumer<Annotation>) StanfordCoreNLP
annotate(Annotation) StanfordCoreNLPClient
Run the pipeline on an input annotation.
annotate(Iterable<Annotation>, int, Consumer<Annotation>) StanfordCoreNLPClient
This method fires off a request to the server.
annotate(Annotation, Consumer<Annotation>) StanfordCoreNLPClient
The canonical entry point of the client annotator.
annotate(Annotation) TokenizerAnnotator
Does the actual work of splitting TextAnnotation into CoreLabels,
which are then attached to the TokensAnnotation.
annotate(Annotation) TokensRegexAnnotator
annotate(Annotation) TokensRegexNERAnnotator
annotate(Annotation) TrueCaseAnnotator
annotate(Annotation) WordsToSentencesAnnotator
If setCountLineNumbers is set to true, we count line numbers by
telling the underlying splitter to return empty lists of tokens
and then treating those empty lists as empty lines.
annotate(Annotation) StatisticalCorefSystem
annotate(Annotation, boolean) StatisticalCorefSystem
annotate(Annotation) GUTimeAnnotator
annotate(CoreMap) GUTimeAnnotator
annotate(Annotation) HeidelTimeAnnotator
annotate(CoreMap) HeidelTimeAnnotator
annotate(Annotation) TimeAnnotator
AnnotateAction(Map<String, String>) CoreMapSequenceMatchAction.AnnotateAction
AnnotateAction(Env) TokenSequenceParser
annotateChunk(CoreMap, List<CoreLabel>, int, int, int) ChunkAnnotationUtils
Annotates a CoreMap representing a chunk with basic chunk information
CharacterOffsetBeginAnnotation - set to CharacterOffsetBeginAnnotation of first token in chunk
CharacterOffsetEndAnnotation - set to CharacterOffsetEndAnnotation of last token in chunk
TokensAnnotation - List of tokens in this chunk
TokenBeginAnnotation - Index of first token in chunk (index in original list of tokens)
tokenStartIndex + totalTokenOffset
TokenEndAnnotation - Index of last token in chunk (index in original list of tokens)
tokenEndIndex + totalTokenOffset
annotateChunk(CoreMap, Class, Class, CoreMapAttributeAggregator) ChunkAnnotationUtils
annotateChunk(CoreMap, Map<String, String>) ChunkAnnotationUtils
annotateChunks(List<? extends CoreMap>, int, int, Map<String, String>) ChunkAnnotationUtils
annotateChunks(List<? extends CoreMap>, Map<String, String>) ChunkAnnotationUtils
annotateChunkText(CoreMap, Class) ChunkAnnotationUtils
Annotates a CoreMap representing a chunk with text information
TextAnnotation - String representing tokens in this chunks (token text separated by space)
annotateChunkText(CoreMap, CoreMap) ChunkAnnotationUtils
Annotates a CoreMap representing a chunk with text information
TextAnnotation - String extracted from the origAnnotation using character offset information for this chunk
annotateChunkTokens(CoreMap, Class, Class) ChunkAnnotationUtils
Annotates tokens in chunk
ANNOTATEDPARSETREE_FIELD_NUMBER CoreNLPProtos.Sentence
AnnotatedTextReader edu.stanford.nlp.patterns.surface
CanNOT handle overlapping labeled text (that is one token cannot belong to
multiple labels)! Note that there has to be spaces around the tags
and for the reader to work correctly!
AnnotatedTextReader() AnnotatedTextReader
annotateGroup(Map<String, String>) CoreMapSequenceMatcher
annotateGroup(int, Map<String, String>) CoreMapSequenceMatcher
AnnotateHeadFunction(HeadFinder) TregexPoweredTreebankParserParams.AnnotateHeadFunction
AnnotateHeadFunction(HeadFinder, boolean) TregexPoweredTreebankParserParams.AnnotateHeadFunction
annotateMulticlass(List<Datum<String, String>>) BasicRelationExtractor
AnnotatePunctuationFunction(String, String) AbstractTreebankParserParams.AnnotatePunctuationFunction
annotateSentence(CoreMap) BasicRelationExtractor
annotateSentence(CoreMap, Map<CoreLabel, List<CoreLabel>>) OpenIE
Annotate a single sentence.
annotateSingleSentence(CoreMap, String, SUTime.TimeIndex) TimeAnnotator
Helper method for people not working from a complete Annotation.
annotateTokenCoref CoNLL2011DocumentReader.Options
annotateTokenCoref CoNLLDocumentReader.Options
annotateTokenNer CoNLL2011DocumentReader.Options
annotateTokenNer CoNLLDocumentReader.Options
annotateTokenPos CoNLL2011DocumentReader.Options
annotateTokenPos CoNLLDocumentReader.Options
annotateTokenSpeaker CoNLL2011DocumentReader.Options
annotateTokenSpeaker CoNLLDocumentReader.Options
annotateTreeCoref CoNLL2011DocumentReader.Options
annotateTreeCoref CoNLLDocumentReader.Options
annotateTreeNer CoNLL2011DocumentReader.Options
annotateTreeNer CoNLLDocumentReader.Options
annotateTrees(List<Tree>) TreebankAnnotator
annotation Document
Document annotation
annotation Document
Document annotation
annotation InputDoc
annotation MatchedExpression
Annotation edu.stanford.nlp.pipeline
An annotation representing a span of text in a document.
Annotation(Annotation) Annotation
Copy constructor.
Annotation(String) Annotation
The text becomes the CoreAnnotations.TextAnnotation of the newly
created Annotation.
Annotation(List<CoreMap>) Annotation
Make a new Annotation from a List of tokenized sentences.
Annotation() Annotation
Deprecated.
Annotation() TimeExpression.Annotation
ANNOTATION_FUNCTION ValueFunctions
AnnotationCreator edu.stanford.nlp.pipeline
Creates a annotation from an input source
AnnotationExtractRule() SequenceMatchRules.AnnotationExtractRule
AnnotationExtractRuleCreator() SequenceMatchRules.AnnotationExtractRuleCreator
annotationField SequenceMatchRules.AnnotationExtractRule
Annotation field to apply rule over: text or tokens or numerizedtokens
AnnotationLookup edu.stanford.nlp.ling
AnnotationLookup.KeyLookup edu.stanford.nlp.ling
AnnotationMatchedFilter(MatchedExpression.SingleAnnotationExtractor) SequenceMatchRules.AnnotationMatchedFilter
AnnotationOutputter edu.stanford.nlp.pipeline
An interface for outputting CoreNLP Annotations to different output
formats.
AnnotationOutputter() AnnotationOutputter
AnnotationOutputter.Options edu.stanford.nlp.pipeline
AnnotationPipeline edu.stanford.nlp.pipeline
This class is designed to apply multiple Annotators
to an Annotation.
AnnotationPipeline(List<Annotator>) AnnotationPipeline
AnnotationPipeline() AnnotationPipeline
annotations TregexPoweredTreebankParserParams
This data structure dictates how an arbitrary tree should be
annotated.
AnnotationSerializer edu.stanford.nlp.pipeline
AnnotationSerializer() AnnotationSerializer
AnnotationSerializer.IntermediateEdge edu.stanford.nlp.pipeline
AnnotationSerializer.IntermediateNode edu.stanford.nlp.pipeline
AnnotationSerializer.IntermediateSemanticGraph edu.stanford.nlp.pipeline
annotationsToSkip BasicEntityExtractor
annotationToDoc(Annotation, StanfordCoreNLP) XMLOutputter
Converts the given annotation to an XML document using options taken from the StanfordCoreNLP pipeline
annotationToDoc(Annotation, AnnotationOutputter.Options) XMLOutputter
Converts the given annotation to an XML document using the specified options
annotationToTmlTextElement(Annotation) SUTimeMain
annotationToXmlDocument(Annotation) SUTimeMain
AnnotationUtils edu.stanford.nlp.ie.machinereading.structure
Utilities to manipulate Annotations storing datasets or sentences with Machine Reading info
Annotator edu.stanford.nlp.pipeline
This is an interface for adding annotations to a partially annotated
Annotation.
Annotator.Requirement edu.stanford.nlp.pipeline
The Requirement is a general way of describing the pre and post
conditions of an Annotator running.
AnnotatorFactories edu.stanford.nlp.pipeline
AnnotatorFactory edu.stanford.nlp.pipeline
A Factory for creating a certain type of Annotator.
AnnotatorFactory(Properties, AnnotatorImplementations) AnnotatorFactory
AnnotatorImplementations edu.stanford.nlp.pipeline
A class abstracting the implementation of various annotators.
AnnotatorImplementations() AnnotatorImplementations
AnnotatorPool edu.stanford.nlp.pipeline
An object for keeping track of Annotators.
AnnotatorPool() AnnotatorPool
Create an empty AnnotatorPool.
announceObjectBankEntries SeqClassifierFlags
anonymyizeNodes(Iterable<IndexedWord>, String) SemanticGraphUtils
GIven an iterable set of distinct vertices, creates a new mapping that maps the
original vertices to a set of "generic" versions.
ansiCode Color
ansiCode Style
ANSWER_FIELD_NUMBER CoreNLPProtos.Token
AnswerAnnotation() CoreAnnotations.AnswerAnnotation
answerClass LearnImportantFeatures
answerFile SeqClassifierFlags
answerLabel LearnImportantFeatures
AnswerObjectAnnotation() CoreAnnotations.AnswerObjectAnnotation
AntecedentAnnotation() CoreAnnotations.AntecedentAnnotation
antecedentIsMentionSpeaker(Document, Mention, Mention, Dictionaries) Rules
Is the speaker for mention the same entity as the ant entity?
antecedentIsMentionSpeaker(Document, Mention, Mention, Dictionaries) Rules
Is the speaker for mention the same entity as the ant entity?
antecedentMatchesMentionSpeakerAnnotation(Mention, Mention) Rules
Is the speaker for mention the same entity as the ant entity?
antecedentMatchesMentionSpeakerAnnotation(Mention, Mention, Document) Rules
The antecedent matches the speaker annotation found in the mention
antecedentMatchesMentionSpeakerAnnotation(Mention, Mention, Document) Rules
The antecedent matches the speaker annotation found in the mention
antecedentOrdering Mention
ANY RobustTokenizer
ANY IntTaggedWord
ANY_DISTANCE_INT IntDependency
ANY_NODE NodePattern
ANY_NODE_PATTERN TokenSequencePattern
ANY_NODE_PATTERN_EXPR SequencePattern
ANY_TAG_INT IntTaggedWord
Any_VALUE CoreNLPProtos.Language
Any = 1;
ANY_WORD_INT IntTaggedWord
AnyNodePattern() NodePattern.AnyNodePattern
aOverAFilter ATBArabicDataset.ArabicRawTreeNormalizer
aOverAFilter BobChrisTreeNormalizer
AOverAFilter() BobChrisTreeNormalizer.AOverAFilter
APOSTROPHE RobustTokenizer
appearEarlierThan(Mention) Mention
Find which mention appears first in a document
appearEarlierThan(Mention) Mention
Find which mention appears first in a document
append(SequenceMatchResult<T>, List) SequenceMatcher.BasicMatchReplacement
Append to replacement list our list of replacement items
append(SequenceMatchResult<T>, List) SequenceMatcher.GroupMatchReplacement
Append to replacement list the matched group with the specified group id
append(SequenceMatchResult<T>, List) SequenceMatcher.MatchReplacement
Append to replacement list
append(SequenceMatchResult<T>, List) SequenceMatcher.NamedGroupMatchReplacement
Append to replacement list the matched group with the specified group name
append(CharSequence) RedwoodPrintStream
append(CharSequence, int, int) RedwoodPrintStream
append(char) RedwoodPrintStream
appendCoreMap(List<T>, CoreMap, String, int, int, CoreTokenFactory<T>) ChunkAnnotationUtils
appendDateFormats(DateTimeFormatterBuilder, int) SUTime.PartialTime
AppendingObjectOutputStream(OutputStream) GenericAnnotationSerializer.AppendingObjectOutputStream
appendLC SeqClassifierFlags
appendOffset(StringBuffer, int) AceElement
appendRules(List<SequenceMatchRules.Rule>) CoreMapExpressionExtractor
Add specified rules to this extractor
appendTimeFormats(DateTimeFormatterBuilder, int) SUTime.PartialTime
appliesNumericClassifiers() NERClassifierCombiner
apply(String) AbstractSequenceClassifier
Maps a String input to an XML-formatted rendition of applying NER to the
String.
apply(String) Buckwalter
apply(List<HasWord>) IBMArabicEscaper
Converts an input list of
HasWord in IBM Arabic to
LDC ATBv3 representation.
apply(String) IBMArabicEscaper
Applies escaping to a single word.
apply(CoreMap) CoreMapNodePatternTrigger
apply(SequenceMatchResult<T>, int...) CoreMapSequenceMatchAction.AnnotateAction
apply(SequenceMatchResult<CoreMap>, int...) CoreMapSequenceMatchAction.MergeAction
apply(CoreMap) MatchedExpression.SingleAnnotationExtractor
apply(List<? extends T>) MultiPatternMatcher.BasicSequencePatternTrigger
apply(SequenceMatchResult<T>, int...) SequenceMatchAction
apply(SequenceMatchResult<T>) SequenceMatchAction.BoundAction
apply(SequenceMatchResult<T>, int...) SequenceMatchAction.BranchAction
apply(SequenceMatchResult<T>, int...) SequenceMatchAction.NextMatchAction
apply(SequenceMatchResult<T>, int...) SequenceMatchAction.SeriesAction
apply(SequenceMatchResult<T>, int...) SequenceMatchAction.StartMatchAction
apply(MR) SequenceMatchResult.GroupToIntervalFunc
apply(CoreMap) SequenceMatchRules.CoreMapFunctionApplier
apply(CoreMap) SequenceMatchRules.CoreMapToListFunctionApplier
apply(List<? extends T>) SequenceMatchRules.MultiSequencePatternExtractRule
apply(SequenceMatchResult<CoreMap>) SequenceMatchRules.SequenceMatchedExpressionExtractor
apply(SequenceMatchResult<T>) SequenceMatchRules.SequenceMatchResultExtractor
apply(List<? extends T>) SequenceMatchRules.SequencePatternExtractRule
apply(MatchResult) SequenceMatchRules.StringMatchedExpressionExtractor
apply(MatchResult) SequenceMatchRules.StringMatchResultExtractor
apply(String) SequenceMatchRules.StringPatternExtractRule
apply(Env, List<Value>) ValueFunction
Applies the function to the list values using the environment as context
and returns the evaluated value.
apply(Env, List<Value>) ValueFunctions.BooleanFunction
apply(Env, List<Value>) ValueFunctions.CompareFunction
apply(Env, List<Value>) ValueFunctions.NumericFunction
apply(Env, List<Value>) ValueFunctions.StringFunction
apply(SemanticGraph, Boolean) ForwardEntailer
Create a new search problem instance, given a sentence (possibly fragment), and the corresponding
parse tree.
apply(X) IdentityFunction
apply(String) ObjectBank.PathToFileFunction
apply(List<? extends HasWord>) ParserGrammar
Parses the list of HasWord.
apply(TregexMatcher) AbstractTreebankParserParams.AnnotatePunctuationFunction
apply(Pair<UnaryGrammar, BinaryGrammar>) LinearGrammarSmoother
Destructively modifies the input and returns it as a convenience.
apply(TregexMatcher) TregexPoweredTreebankParserParams.AnnotateHeadFunction
apply(TregexMatcher) TregexPoweredTreebankParserParams.SimpleStringFunction
apply(Configuration, String) ArcStandard
apply(Configuration, String) ParsingSystem
Apply the given transition to the given configuration, modifying
the configuration's state in place.
apply(State) BinaryTransition
Add a binary node to the existing node on top of the stack
apply(State, double) BinaryTransition
Add a binary node to the existing node on top of the stack
apply(State) CompoundUnaryTransition
Add a unary node to the existing node on top of the stack
apply(State, double) CompoundUnaryTransition
Add a unary node to the existing node on top of the stack
apply(State) FinalizeTransition
apply(State, double) FinalizeTransition
apply(State) IdleTransition
Do nothing
apply(State, double) IdleTransition
Do nothing
apply(State) ShiftTransition
Add the new preterminal to the stack, increment the queue position.
apply(State, double) ShiftTransition
Add the new preterminal to the stack, increment the queue position.
apply(State) Transition
Applies this transition to get a new state.
apply(State, double) Transition
Applies this transition to get a new state.
apply(State) UnaryTransition
Add a unary node to the existing node on top of the stack
apply(State, double) UnaryTransition
Add a unary node to the existing node on top of the stack
apply(List<? extends CoreMap>) CoreMapAggregator
apply(HasWord) Americanize
Americanize the HasWord or String coming in.
apply(String) AmericanizeFunction
apply(String) LowercaseAndAmericanizeFunction
apply(String) LowercaseFunction
apply(Object) Morphology
apply(List<IN>) PTBEscapingProcessor
Escape a List of HasWords.
apply(String) TrueCasingForNISTDocumentReaderAndWriter.LineToTrueCasesParser
apply(String) MultiClassPrecisionRecallExtendedStats.StringStringConverter
apply(List<? extends HasWord>) Tagger
apply(List<? extends HasWord>) MaxentTagger
Expects a sentence and returns a tagged sentence.
apply(SUTime.Temporal, SUTime.Temporal, int) SUTime.TemporalOp
apply(SUTime.Temporal, SUTime.Temporal) SUTime.TemporalOp
apply(SUTime.Temporal...) SUTime.TemporalOp
apply(Object...) SUTime.TemporalOp
apply(String) TimeFormatter.CustomDateFormatExtractor
apply(MatchResult) TimeFormatter.CustomDateFormatExtractor
apply(CoreMap) TimeFormatter.JavaDateFormatExtractor
apply(CoreMap) TimeFormatter.JodaDateTimeFormatExtractor
apply(Tree) BasicCategoryTreeTransformer
apply(TreeVisitor) CompositeTreebank
apply(TreeVisitor) DiskTreebank
Applies the TreeVisitor to to all trees in the Treebank.
apply(TreeVisitor) FilteringTreebank
Applies the TreeVisitor, but only to the trees that pass the
filter.
apply(List<HasWord>) ChineseEscaper
Note: At present this clobbers the input list items.
apply(TreeVisitor) MemoryTreebank
Apply the TreeVisitor tp to all trees in the Treebank.
apply(TreeVisitor) TransformingTreebank
Applies the TreeVisitor to to all trees in the Treebank.
apply(TreeVisitor) Treebank
Apply a TreeVisitor to each tree in the Treebank.
apply(String) Color
apply() RedwoodConfiguration
Apply this configuration to Redwood
apply(Properties) RedwoodConfiguration
Parses a properties file and applies it immediately to Redwood
apply(RedwoodConfiguration, Redwood.RecordHandlerTree) RedwoodConfiguration.Thunk
apply(Properties) StanfordRedwoodConfiguration
Configures the Redwood logger using a reasonable set of defaults,
which can be overruled by the supplied Properties file
apply(String) UTF8EquivalenceFunction
APPLY_NUMERIC_CLASSIFIERS_DEFAULT NERClassifierCombiner
APPLY_NUMERIC_CLASSIFIERS_PROPERTY NERClassifierCombiner
APPLY_NUMERIC_CLASSIFIERS_PROPERTY_BASE NERClassifierCombiner
applyCompositeRule(SequenceMatchRules.ExtractRule<List<? extends CoreMap>, T>, List<? extends CoreMap>, List<T>, int) CoreMapExpressionExtractor
ApplyDepPatterns E extends Pattern > - Class in edu.stanford.nlp.patterns.dep
Applying Dependency patterns to sentences.
ApplyDepPatterns(Map<String, DataInstance>, List<String>, Map<SemgrexPattern, E>, String, boolean, boolean, ConstantsAndVariables) ApplyDepPatterns
applyFeatureCountThreshold(List<Pair<Pattern, Integer>>) Dataset
Applies feature count thresholds to the Dataset.
applyFeatureCountThreshold(int) GeneralDataset
Applies a feature count threshold to the Dataset.
applyFeatureCountThreshold(int) RVFDataset
Applies a feature count threshold to the RVFDataset.
applyFeatureMaxCountThreshold(int) GeneralDataset
Applies a max feature count threshold to the Dataset.
applyFeatureMaxCountThreshold(int) RVFDataset
Applies a feature max count threshold to the RVFDataset.
applyInitialHessian(double[]) QNMinimizer.QNInfo
applyNERPenalty SeqClassifierFlags
applyNumericClassifiers SeqClassifierFlags
applyPats(Counter<E>, String, TwoDimensionalCounter<CandidatePhrase, E>, CollectionValuedMap<E, Triple<String, Integer, Integer>>, Set<CandidatePhrase>) ScorePhrases
ApplyPatterns E extends Pattern > - Class in edu.stanford.nlp.patterns.surface
Applying SurfacePattern to sentences.
ApplyPatterns(Map<String, DataInstance>, List<String>, Map<TokenSequencePattern, E>, String, boolean, boolean, ConstantsAndVariables) ApplyPatterns
applyPrior(double[], double) CRFLogConditionalObjectiveFunction
applySpecializedNER(List<E>) QuantifiableEntityNormalizer
Runs a deterministic named entity classifier which is good at recognizing
numbers and money and date expressions not recognized by our statistical
NER.
applyTo(SemanticGraph, ClauseSplitterSearchProblem.State, SemanticGraphEdge, SemanticGraphEdge, SemanticGraphEdge) ClauseSplitterSearchProblem.Action
Apply this action to the given state.
applyToProjections(TreeVisitor, Tree, Tree, HeadFinder) Trees
applyToTruthValue(boolean) NaturalLogicRelation
Implements the finite state automata of composing the truth value of a sentence with a natural logic relation being
applied.
APPOSITIONAL_MODIFIER EnglishGrammaticalRelations
The "appositional modifier" grammatical relation.
APPOSITIONAL_MODIFIER ChineseGrammaticalRelations
The "appositional modifier" (appos) grammatical relation (abstract).
APPOSITIONAL_MODIFIER UniversalEnglishGrammaticalRelations
The "appositional modifier" grammatical relation.
appositions Mention
Set of other mentions in the same sentence that are syntactic appositions to this
appositions Mention
Set of other mentions in the same sentence that are syntactic appositions to this
APPOSITIONS_FIELD_NUMBER CoreNLPProtos.Mention
APPROVE_OPTION OpenPageDialog
approx SUTime.Temporal
approxAccuracy(int[][], int[][]) AbstractEvaluate
approxCombinedAccuracy(int[][], int[][]) AbstractEvaluate
APPROXIMATE TaggerConfig
approximate JodaTimeUtils.ConversionOptions
Treat durations as approximate
APPROXIMATE RepeatedRecordHandler
APPROXIMATE_EQUIVALENCE_CLASSES RNNOptions
ApproximateRepeatSemantics() RepeatedRecordHandler.ApproximateRepeatSemantics
ApproxMatch K ,V > - Class in edu.stanford.nlp.ling.tokensregex.matcher
Represents an approximate match with a cost
ApproxMatch() ApproxMatch
ApproxMatch(List<K>, V, int, int, double) ApproxMatch
ApproxMatch(List<K>, V, int, int, List<Match<K, V>>, double) ApproxMatch
ApproxMatch(List<K>, V, int, int, List<Match<K, V>>, double, Interval[]) ApproxMatch
APRIL SUTime
ARABIC FontDetector
Arabic_VALUE CoreNLPProtos.Language
Arabic = 2;
arabicAoverAFilter(Tree) ATBArabicDataset.ArabicRawTreeNormalizer
arabicDigit DefaultLexicalMapper
ArabicDocumentReaderAndWriter edu.stanford.nlp.international.arabic.process
Reads newline delimited UTF-8 Arabic sentences with or without
gold segmentation markers.
ArabicDocumentReaderAndWriter(boolean) ArabicDocumentReaderAndWriter
ArabicDocumentReaderAndWriter(boolean, TokenizerFactory<CoreLabel>) ArabicDocumentReaderAndWriter
ArabicDocumentReaderAndWriter(boolean, boolean, TokenizerFactory<CoreLabel>) ArabicDocumentReaderAndWriter
ArabicDocumentReaderAndWriter(boolean, boolean, boolean, String, TokenizerFactory<CoreLabel>) ArabicDocumentReaderAndWriter
ArabicDocumentReaderAndWriter(boolean, boolean, boolean, String, boolean, TokenizerFactory<CoreLabel>) ArabicDocumentReaderAndWriter
ArabicDocumentReaderAndWriter.RewrittenArabicAnnotation edu.stanford.nlp.international.arabic.process
ArabicEmptyFilter() ArabicTreeNormalizer.ArabicEmptyFilter
ArabicHeadFinder edu.stanford.nlp.trees.international.arabic
Find the head of an Arabic tree, using the usual kind of heuristic
head finding rules.
ArabicHeadFinder() ArabicHeadFinder
ArabicHeadFinder(String) ArabicHeadFinder
Construct an ArabicHeadFinder with a String parameter corresponding to the tagset in use.
ArabicHeadFinder(ArabicHeadFinder.TagSet) ArabicHeadFinder
ArabicHeadFinder(TreebankLanguagePack) ArabicHeadFinder
ArabicHeadFinder(TreebankLanguagePack, ArabicHeadFinder.TagSet) ArabicHeadFinder
ArabicHeadFinder.TagSet edu.stanford.nlp.trees.international.arabic
ArabicMorphoFeatures() ArabicMorphoFeatureSpecification.ArabicMorphoFeatures
ArabicMorphoFeatureSpecification edu.stanford.nlp.international.arabic
Extracts morphosyntactic features from BAMA/SAMA analyses.
ArabicMorphoFeatureSpecification() ArabicMorphoFeatureSpecification
ArabicMorphoFeatureSpecification.ArabicMorphoFeatures edu.stanford.nlp.international.arabic
Converts features specifications to labels for tagging
arabicPunc DefaultLexicalMapper
ArabicRawTreeNormalizer(PrintWriter, PrintWriter) ATBArabicDataset.ArabicRawTreeNormalizer
ArabicRawTreeReaderFactory() ArabicTreeReaderFactory.ArabicRawTreeReaderFactory
ArabicRawTreeReaderFactory(boolean) ArabicTreeReaderFactory.ArabicRawTreeReaderFactory
ArabicSegmenter edu.stanford.nlp.international.arabic.process
Arabic word segmentation model based on conditional random fields (CRF).
ArabicSegmenter(Properties) ArabicSegmenter
Make an Arabic Segmenter.
ArabicSegmenter(ArabicSegmenter) ArabicSegmenter
Copy constructor.
ArabicSegmenterFeatureFactory IN extends CoreLabel > - Class in edu.stanford.nlp.international.arabic.process
Feature factory for the IOB clitic segmentation model described by
Green and DeNero (2012).
ArabicSegmenterFeatureFactory() ArabicSegmenterFeatureFactory
ArabicSubcategoryStripper() ArabicTreebankParserParams.ArabicSubcategoryStripper
ArabicTokenizer T extends HasWord > - Class in edu.stanford.nlp.international.arabic.process
Tokenizer for UTF-8 Arabic.
ArabicTokenizer(Reader, LexedTokenFactory<T>, Properties) ArabicTokenizer
ArabicTokenizer.ArabicTokenizerFactory T extends HasWord > - Class in edu.stanford.nlp.international.arabic.process
ArabicTreebankLanguagePack edu.stanford.nlp.trees.international.arabic
Specifies the treebank/language specific components needed for
parsing the Penn Arabic Treebank (ATB).
ArabicTreebankLanguagePack() ArabicTreebankLanguagePack
ArabicTreebankParserParams edu.stanford.nlp.parser.lexparser
ArabicTreebankParserParams() ArabicTreebankParserParams
ArabicTreebankParserParams.ArabicSubcategoryStripper edu.stanford.nlp.parser.lexparser
ArabicTreebankTokenizer edu.stanford.nlp.trees.international.arabic
Builds a tokenizer for the Penn Arabic Treebank (ATB) using a
StreamTokenizer.
ArabicTreebankTokenizer(Reader) ArabicTreebankTokenizer
ArabicTreeNormalizer edu.stanford.nlp.trees.international.arabic
Normalizes both terminals and non-terminals in Penn Arabic Treebank (ATB)
trees.
ArabicTreeNormalizer(boolean, boolean, boolean, boolean, boolean) ArabicTreeNormalizer
ArabicTreeNormalizer(boolean, boolean, boolean) ArabicTreeNormalizer
ArabicTreeNormalizer(boolean, boolean) ArabicTreeNormalizer
ArabicTreeNormalizer(boolean) ArabicTreeNormalizer
ArabicTreeNormalizer() ArabicTreeNormalizer
ArabicTreeNormalizer.ArabicEmptyFilter edu.stanford.nlp.trees.international.arabic
Remove traces and pronoun deletion markers.
ArabicTreeReaderFactory edu.stanford.nlp.trees.international.arabic
Reads ArabicTreebank trees.
ArabicTreeReaderFactory() ArabicTreeReaderFactory
ArabicTreeReaderFactory(boolean, boolean, boolean, boolean, boolean, boolean, boolean) ArabicTreeReaderFactory
ArabicTreeReaderFactory.ArabicRawTreeReaderFactory edu.stanford.nlp.trees.international.arabic
ArabicUnknownWordModel edu.stanford.nlp.parser.lexparser
This is a basic unknown word model for Arabic.
ArabicUnknownWordModel(Options, Lexicon, Index<String>, Index<String>, ClassicCounter<IntTaggedWord>) ArabicUnknownWordModel
ArabicUnknownWordModel(Options, Lexicon, Index<String>, Index<String>) ArabicUnknownWordModel
This constructor creates an UWM with empty data structures.
ArabicUnknownWordModelTrainer edu.stanford.nlp.parser.lexparser
ArabicUnknownWordModelTrainer() ArabicUnknownWordModelTrainer
arbitraryInputLayerSize SeqClassifierFlags
Arc(TransducerGraph.Arc<NODE, IN, OUT>) TransducerGraph.Arc
Arc(NODE, NODE) TransducerGraph.Arc
Arc(NODE, NODE, IN) TransducerGraph.Arc
Arc(NODE, NODE, IN, OUT) TransducerGraph.Arc
ARCH TaggerConfig
ArcStandard edu.stanford.nlp.parser.nndep
Defines an arc-standard transition-based dependency parsing system
(Nivre, 2004).
ArcStandard(TreebankLanguagePack, List<String>, boolean) ArcStandard
areTransitionsEqual(State) State
Whether or not the transitions that built the two states are
equal.
arg IntDependency
ARG_FIELD_NUMBER CoreNLPProtos.Relation
argCounter MLEDependencyGrammar
Stores all the counts for dependencies (with and without the word
being a wildcard) in the reduced tag space.
ArgDescendentAnnotation() CoreAnnotations.ArgDescendentAnnotation
argmax(double[]) ArrayMath
argmax(float[]) ArrayMath
argmax(int[]) ArrayMath
argmax(Counter<E>) Counters
Finds and returns the key in the Counter with the largest count.
argmax(Counter<E>, Comparator<E>) Counters
Finds and returns the key in the Counter with the largest count.
argmax(Counter<E>, Comparator<E>, E) Counters
Finds and returns the key in the Counter with the largest count.
argmax() Distribution
argmax(Comparator<E>) IntCounter
Finds and returns the key in this Counter with the largest count.
argmax() IntCounter
Finds and returns the key in this Counter with the largest count.
argmax_tieLast(double[]) ArrayMath
argmin(double[]) ArrayMath
argmin(float[]) ArrayMath
argmin(int[]) ArrayMath
argmin(Counter<E>) Counters
Finds and returns the key in this Counter with the smallest count.
argmin(Counter<E>, Comparator<E>) Counters
Finds and returns the key in this Counter with the smallest count.
argmin(Comparator<E>) IntCounter
Finds and returns the key in this Counter with the smallest count.
argmin() IntCounter
Finds and returns the key in this Counter with the smallest count.
ARGNAME_FIELD_NUMBER CoreNLPProtos.Relation
argNames RelationMention
List of argument names in this relation
args RelationMention
List of arguments in this relation
If unnamed, arguments MUST be stored in semantic order, e.g., ARG0 must be a person in a employed-by relation
argsMatch(RelationMention) RelationMention
argsMatch(ExtractionObject...) RelationMention
argsMatch(List<ExtractionObject>) RelationMention
Verifies if the two sets of arguments match
argsToMap(String[]) StringUtils
Parses command line arguments into a Map.
argsToMap(String[], Map<String, Integer>) StringUtils
Parses command line arguments into a Map.
argsToProperties(String...) StringUtils
In this version each flag has zero or one argument.
argsToProperties(String[], Map<String, Integer>) StringUtils
argsToPropertiesWithResolve(String[]) StringUtils
convert args to properties with variable names resolved.
ARGUMENT EnglishGrammaticalRelations
The "argument" grammatical relation.
ARGUMENT ChineseGrammaticalRelations
The "argument" (arg) grammatical relation (abstract).
ARGUMENT UniversalEnglishGrammaticalRelations
The "argument" grammatical relation.
ArgumentAnnotation() CoreAnnotations.ArgumentAnnotation
ArgUtils edu.stanford.nlp.parser.common
Utility methods or common blocks of code for dealing with parser
arguments, such as extracting Treebank information
ARMY_TIME_MORNING NumberSequenceClassifier
arrange(Annotation, List<List<CoreLabel>>, List<Tree>, List<List<Mention>>) MentionExtractor
arrange(Annotation, List<List<CoreLabel>>, List<Tree>, List<List<Mention>>, List<List<Mention>>, boolean) MentionExtractor
arrange(Annotation, List<List<CoreLabel>>, List<Tree>, List<List<Mention>>, boolean) MentionExtractor
Post-processes the extracted mentions.
ARRAY_LIST_FACTORY CollectionFactory
ArrayCoreMap edu.stanford.nlp.util
Base implementation of
CoreMap backed by two Java arrays.
ArrayCoreMap() ArrayCoreMap
Default constructor - initializes with default initial annotation
capacity of 4.
ArrayCoreMap(int) ArrayCoreMap
Initializes this ArrayCoreMap, pre-allocating arrays to hold
up to capacity key,value pairs.
ArrayCoreMap(ArrayCoreMap) ArrayCoreMap
Copy constructor.
ArrayCoreMap(CoreMap) ArrayCoreMap
Copy constructor.
ArrayHeap E > - Class in edu.stanford.nlp.util
Implements a heap as an ArrayList.
ArrayHeap(Comparator<? super E>) ArrayHeap
Create an ArrayHeap.
ArrayHeap(Comparator<? super E>, int) ArrayHeap
arrayListFactory() CollectionFactory
Return a factory for making ArrayList Collections.
arrayListFactory(int) CollectionFactory
ArrayListFactory() CollectionFactory.ArrayListFactory
ArrayMap K ,V > - Class in edu.stanford.nlp.util
Map backed by an Array.
ArrayMap() ArrayMap
ArrayMap(int) ArrayMap
ArrayMap(Map<? extends K, ? extends V>) ArrayMap
ArrayMap(K[], V[]) ArrayMap
arrayMapFactory() MapFactory
Return a MapFactory that returns an ArrayMap.
ArrayMath edu.stanford.nlp.math
Class ArrayMath
ArrayMath.InvalidElementException edu.stanford.nlp.math
ArraySet E > - Class in edu.stanford.nlp.util
An array-backed set.
ArraySet() ArraySet
Constructs an ArraySet.
ArraySet(int) ArraySet
Constructs an ArraySet with specified initial size of backing array.
ArraySet(E...) ArraySet
Constructs an ArraySet with the specified elements.
arraySize ExhaustivePCFGParser
ArrayStringFilter edu.stanford.nlp.util
Filters Strings based on whether they exactly match any string in
the array it is initially constructed with.
ArrayStringFilter(ArrayStringFilter.Mode, String...) ArrayStringFilter
ArrayStringFilter.Mode edu.stanford.nlp.util
arrayToFile(double[], String) StochasticDiffFunctionTester
ArrayUtils edu.stanford.nlp.util
Static utility methods for operating on arrays.
asAnnotation() Document
Return this Document as an Annotation object.
asArray(Counter<E>, Index<E>) Counters
Convert a counter to an array using a specified key index.
asArray(Counter<E>, Index<E>, int) Counters
Convert a counter to an array using a specified key index.
asArray(Counter<E>) Counters
Convert a counter to an array, the order of the array is random
asArrayList(Iterator<? extends T>) Iterables
Creates an ArrayList containing all of the Objects returned by the given Iterator.
ASBCunkDict edu.stanford.nlp.tagger.maxent
ASCENDING_COMPARATOR ScoredComparator
asCharacterArray(String) Characters
Map a String to an array of type Character.
ASCII ChineseUtils
ASCII_QUOTES QuoteAnnotator
asCollection(Iterator<? extends E>, CollectionFactory<E>) Iterables
Creates a new Collection from the given CollectionFactory, and adds all of the Objects
returned by the given Iterator.
asCoreLabels(Function<Sentence, Object>...) Sentence
Returns this sentence as a list of CoreLabels representing the sentence.
asCoreMap(Function<Sentence, Object>...) Sentence
Returns this sentence as a CoreNLP CoreMap object.
asCounter(Collection<E>) Counters
Takes in a Collection of something and makes a counter, incrementing once
for each object in the collection.
asCounter(FixedPrioritiesPriorityQueue<E>) Counters
Returns a counter whose keys are the elements in this priority queue, and
whose counts are the priorities in this queue.
asDependencyTree() RelationTriple
An optional method, returning the dependency tree this triple was extracted from
asDependencyTree() RelationTriple.WithTree
An optional method, returning the dependency tree this triple was extracted from
asDOTString() TransducerGraph
asDoubleArray(Collection<Double>) CollectionUtils
asExpression(Env, Object) Expressions
asFeatures() CRFDatum
Returns the collection that this BasicDatum was constructed with.
asFeatures() BasicDatum
Returns the collection that this BasicDatum was constructed with.
asFeatures() BasicDocument
Returns this (the features are the list of words).
asFeatures() Featurizable
returns Object as a Collection of its features
asFeatures() RVFDatum
Returns the list of features without values
asFeaturesCounter() RVFDatum
Returns the Counter of features and values
asFeatureVals() CRFDatum
Returns the double array containing the feature values
asHashSet(Iterator<? extends T>) Iterables
Creates a HashSet containing all of the Objects returned by the given Iterator.
asImmutableSet(T[]) ArrayUtils
Return an immutable Set containing the same elements as the specified
array.
asIntArray(Collection<Integer>) CollectionUtils
asList() ArrayHeap
asList(int[]) ArrayUtils
needed because Arrays.asList() won't to autoboxing,
so if you give it a primitive array you get a
singleton list back with just that array as an element.
asList(int[]) CollectionUtils
asList(double[]) CollectionUtils
asList() Pair
asList() Quadruple
asList() TreeShapedStack
Returns the current stack as a list
asList() Triple
asMap(Counter<E>) Counters
Returns a map view of the given counter.
asMap(Properties) PropertiesUtils
Tired of Properties not behaving like Map<String,String>s? This method will solve that problem for you.
asNormalizedCounter(C) Counters
L1 normalize a counter.
asObject(Env, Object) Expressions
ASPECT_MARKER ChineseGrammaticalRelations
The "aspect marker" grammatical relation.
asPrimitiveDoubleArray(Collection<Double>) ArrayUtils
asPrimitiveIntArray(Collection<Integer>) ArrayUtils
asProperties(String...) PropertiesUtils
Create a Properties object from the passed in String arguments.
asSentence() RelationTriple
Return the given relation triple as a flat sentence
assertFinite(double[], String) ArrayMath
asSet(T[]) ArrayUtils
Return a Set containing the same elements as the specified array.
asSet(T...) CollectionUtils
Returns a new Set containing all the objects in the specified array.
assign(Expression) AssignableExpression
assign(Expression) Expressions.AssignableFunctionCallExpression
assign(Expression) Expressions.RegexMatchVarExpression
assign(Expression) Expressions.VarExpression
AssignableExpression(Env) TokenSequenceParser
AssignableExpression edu.stanford.nlp.ling.tokensregex.types
This interface represents an expression that can be assigned to.
AssignableFunctionCallExpression(String, List<Expression>, String...) Expressions.AssignableFunctionCallExpression
AssignableNestedVarExpression(Env) TokenSequenceParser
AssignableVar(Env) TokenSequenceParser
assignGroupIds(int) SequencePattern.AndPatternExpr
assignGroupIds(int) SequencePattern.BackRefPatternExpr
assignGroupIds(int) SequencePattern.GroupPatternExpr
assignGroupIds(int) SequencePattern.MultiNodePatternExpr
assignGroupIds(int) SequencePattern.NodePatternExpr
assignGroupIds(int) SequencePattern.OrPatternExpr
assignGroupIds(int) SequencePattern.PatternExpr
Assigns group ids to groups embedded in this patterns starting with at the specified number,
returns the next available group id.
assignGroupIds(int) SequencePattern.RepeatPatternExpr
assignGroupIds(int) SequencePattern.SequencePatternExpr
assignGroupIds(int) SequencePattern.SpecialNodePatternExpr
assignGroupIds(int) SequencePattern.ValuePatternExpr
assignIds RuleBasedCorefMentionFinder
assignMentionIDs(List<List<Mention>>, int) RuleBasedCorefMentionFinder
assignMentionIDs(List<List<Mention>>, int) DependencyCorefMentionFinder
AssignmentRule(Env) TokenSequenceParser
AssignmentRule(AssignableExpression, Expression) SequenceMatchRules.AssignmentRule
assignOriginalID() Document
When there is no mentionID information (without gold annotation), assign mention IDs
assignSyntacticHead(EntityMention, Tree, List<CoreLabel>, boolean) GenericDataSetReader
Find the index of the head of an entity.
ASSOCIATIVE_MODIFIER ChineseGrammaticalRelations
The "associative modifier" (assmod) grammatical relation (Chinese-specific).
asSortedList() Beam
asString(Properties) PropertiesUtils
Convert from Properties to String.
assumedTruth ClauseSplitterSearchProblem
The assumed truth of the original clause.
assumedTruth SentenceFragment
The assumed truth of this fragment; this is relevant for what entailments are supported
ASSUMEDTRUTH_FIELD_NUMBER CoreNLPProtos.SentenceFragment
aStringArrToByteArr(String[]) ConvertByteArray
aStringArrToByteArr(String[], int, int) ConvertByteArray
aStringArrToByteArr(byte[], int, String[], int, int) ConvertByteArray
asValue(Env, Object) Expressions
AT RobustTokenizer
ATBArabicDataset edu.stanford.nlp.international.arabic.pipeline
Converts raw ATB trees into a format appropriate for treebank parsing.
ATBArabicDataset() ATBArabicDataset
ATBArabicDataset.ArabicRawTreeNormalizer edu.stanford.nlp.international.arabic.pipeline
ATBCorrector edu.stanford.nlp.international.arabic.pipeline
Makes ATB trees consistent with ArabicTreebankLanguagePack.
ATBCorrector() ATBCorrector
ATBCorrectorVisitor edu.stanford.nlp.international.arabic.pipeline
Wrapper class for using the ATBCorrector class with TreebankPipeline's
TVISITOR parameter.
ATBCorrectorVisitor() ATBCorrectorVisitor
atbFactory() ArabicTokenizer
ATBTreeUtils edu.stanford.nlp.trees.international.arabic
Various static convenience methods for processing Arabic parse trees.
attachedNegatedVerb(IndexedWord) SemanticGraph
attachLabels(Tree, Class<? extends CoreAnnotation<Integer>>) SentimentUtils
attachModelToLexicalizedParser() DVParser
attributeMap ExtractionObject
This stores any optional attributes of ExtractionObjects
attributeMap() ExtractionObject
attributes XMLUtils.XMLTag
attributesAgree(Mention, Dictionaries) Mention
Detects if the mention and candidate antecedent agree on all attributes respectively.
attributesAgree(Mention, Dictionaries) Mention
Detects if the mention and candidate antecedent agree on all attributes respectively.
AttributesEqualMatchChecker(K...) ComplexNodePattern.AttributesEqualMatchChecker
AttributesEqualMatchChecker(Class...) CoreMapNodePattern.AttributesEqualMatchChecker
attrs LatticeEdge
AttrValue(Env, Map<String, String>) TokenSequenceParser
aType Sieve
type of mention we want to compare to.
ATYPE_PROP CorefProperties
aTypeStr Sieve
augmentedDateChars SeqClassifierFlags
AUGUST SUTime
AuthorAnnotation() CoreAnnotations.AuthorAnnotation
AutomatonMinimizer edu.stanford.nlp.fsm
AUX_MODIFIER EnglishGrammaticalRelations
The "auxiliary" grammatical relation.
AUX_MODIFIER ChineseGrammaticalRelations
The "auxiliary" (aux) grammatical relation.
AUX_MODIFIER UniversalEnglishGrammaticalRelations
An auxiliary of a clause is a non-main verb of the clause,
e.g., a modal auxiliary, or a form of be, do or have in a
periphrastic tense.
AUX_PASSIVE_MODIFIER EnglishGrammaticalRelations
The "passive auxiliary" grammatical relation.
AUX_PASSIVE_MODIFIER ChineseGrammaticalRelations
The "auxiliary passive" (auxpass) grammatical relation.
AUX_PASSIVE_MODIFIER UniversalEnglishGrammaticalRelations
The "passive auxiliary" grammatical relation.
auxDataPath MachineReadingProperties
auxReader MachineReading
auxTrueCaseModels SeqClassifierFlags
available() ReaderInputStream
average(double[]) ArrayMath
average(Counter<E>, Counter<E>) Counters
Returns a new Counter with counts averaged from the two given Counters.
averageCount() IntCounter
Returns the mean of all the counts (totalCount/size).
averagedModels ShiftReduceTrainOptions
If set to 0, training outputs the last model produced, regardless
of its score.
averageModels(Collection<PerceptronModel>) PerceptronModel
averagePerceptron SeqClassifierFlags
averageScoredModels(Collection<ScoredObject<PerceptronModel>>) PerceptronModel
awaitTermination(long, TimeUnit) InterruptibleMulticoreWrapper
After a shutdown request, await for the final termination of all
threads.
C SVMLightClassifierFactory
C can be tuned using held-out set or cross-validation
For binary SVM, if C=0, svmlight uses default of 1/(avg x*x)
c ReaderIteratorFactory
The underlying Collection of input sources.
CacheMap K ,V > - Class in edu.stanford.nlp.util
A fixed size hash map with LRU replacement.
CacheMap(int, float, boolean, String) CacheMap
Constructor.
CacheMap(int, float, boolean) CacheMap
CacheMap(int, float) CacheMap
CacheMap(int) CacheMap
cacheNGrams SeqClassifierFlags
CacheParseHypotheses edu.stanford.nlp.parser.dvparser
CacheParseHypotheses(LexicalizedParser) CacheParseHypotheses
calculate(double[]) AdaptedGaussianPriorObjectiveFunction
Calculate the conditional likelihood.
calculate(double[]) BiasedLogConditionalObjectiveFunction
calculate(double[]) BiasedLogisticObjectiveFunction
calculate(double[]) GeneralizedExpectationObjectiveFunction
calculate(double[]) LogConditionalEqConstraintFunction
calculate(double[]) LogConditionalObjectiveFunction
Calculate the conditional likelihood.
calculate(double[]) LogisticObjectiveFunction
calculate(double[]) SemiSupervisedLogConditionalObjectiveFunction
calculate(float[]) CRFLogConditionalObjectiveFloatFunction
calculate(double[]) CRFLogConditionalObjectiveFunction
Calculates both value and partial derivatives at the point x, and save them internally.
calculate(double[]) CRFLogConditionalObjectiveFunctionForLOP
Calculates both value and partial derivatives at the point x, and save them internally.
calculate(double[]) CRFLogConditionalObjectiveFunctionNoisyLabel
Calculates both value and partial derivatives at the point x, and save them internally.
calculate(double[]) CRFLogConditionalObjectiveFunctionWithDropout
Calculates both value and partial derivatives at the point x, and save them internally.
calculate(double[]) CRFNonLinearLogConditionalObjectiveFunction
Calculates both value and partial derivatives at the point x, and save them internally.
calculate(double[]) CRFNonLinearSecondOrderLogConditionalObjectiveFunction
Calculates both value and partial derivatives at the point x, and save them internally.
calculate(float[]) AbstractCachingDiffFloatFunction
Calculate the value at x and the derivative and save them in the respective fields
calculate(double[]) AbstractCachingDiffFunction
Calculate the value at x and the derivative
and save them in the respective fields.
calculate(double[]) DVParserCostAndGradient
calculate(double[]) SentimentCostAndGradient
CALCULATE_IMPORTANCE_PROP CorefProperties
calculateFeatureImportance(Properties) CorefProperties
calculateHeadSpan GenericDataSetReader
If true, sets the head span to match the syntactic head of the extent.
calculatePrecision(Document) CorefScorer
calculatePrecision(Document) ScorerBCubed
calculatePrecision(Document) ScorerMUC
calculatePrecision(Document) ScorerPairwise
calculateProbs(double[][][]) TestSentence
calculateProbs puts log probs of taggings in the probabilities array.
calculateRecall(Document) CorefScorer
calculateRecall(Document) ScorerBCubed
calculateRecall(Document) ScorerMUC
calculateRecall(Document) ScorerPairwise
calculateRVF(double[]) BiasedLogisticObjectiveFunction
calculateRVF(double[]) LogisticObjectiveFunction
calculateScore(Document) CorefScorer
calculatesHessianVectorProduct() StochasticCalculateMethods
calculateStochastic(double[], double[], int[]) LogConditionalObjectiveFunction
This function is used to come up with an estimate of the value / gradient based on only a small
portion of the data (referred to as the batchSize for lack of a better term.
calculateStochastic(double[], double[], int[]) CRFLogConditionalObjectiveFunction
calculateStochastic(double[], double[], int[]) AbstractStochasticCachingDiffFunction
calculateStochastic needs to calculate a stochastic approximation to the derivative and value of
of a function for a given batch of the data.
calculateStochasticAlgorithmicDifferentiation(double[], double[], int[]) LogConditionalObjectiveFunction
calculateStochasticFiniteDifference(double[], double[], double, int[]) LogConditionalObjectiveFunction
calculateStochasticGradient(double[], int[]) LogConditionalObjectiveFunction
calculateStochasticGradient(double[], int[]) CRFLogConditionalObjectiveFunction
Performs stochastic gradient update based
on samples indexed by batch, but does not apply regularization.
calculateStochasticGradient(double[], int[]) AbstractStochasticCachingDiffUpdateFunction
Performs stochastic gradient calculation based
on samples indexed by batch and does not apply regularization.
calculateStochasticGradient(double[], int) AbstractStochasticCachingDiffUpdateFunction
Performs stochastic gradient updates based
on samples indexed by batch and do not apply regularization.
calculateStochasticGradientLocal(double[], int[]) LogConditionalObjectiveFunction
calculateStochasticUpdate(double[], double, int[], double) LogConditionalObjectiveFunction
calculateStochasticUpdate(double[], double, int[], double) CRFLogConditionalObjectiveFunction
Performs stochastic update of weights x (scaled by xScale) based
on samples indexed by batch.
calculateStochasticUpdate(double[], double, int[], double) AbstractStochasticCachingDiffUpdateFunction
Performs stochastic update of weights x (scaled by xScale) based
on samples indexed by batch.
calculateStochasticUpdate(double[], double, int, double) AbstractStochasticCachingDiffUpdateFunction
Performs stochastic update of weights x (scaled by xScale) based
on next batch of batchSize.
calculateWeird1(float[]) CRFLogConditionalObjectiveFloatFunction
CalendarAnnotation() CoreAnnotations.CalendarAnnotation
call() ApplyDepPatterns
call() GetPatternsFromDataMultiClass.LabelWithSeedWords
call() ApplyPatterns
call() CreatePatterns.CreatePatternsThread
CALL_FUNCTION ValueFunctions
callback(Object...) CallbackFunction
CallbackFunction edu.stanford.nlp.util
A callback function (along the lines of Berkeley optimization repo), which is currently used in the optimization package.
CallbackFunction() CallbackFunction
canAddArc(Object, Object, Object, Object) TransducerGraph
canApply(Configuration, String) ArcStandard
canApply(Configuration, String) ParsingSystem
Determine whether the given transition is legal for this
configuration.
CANCEL_OPTION OpenPageDialog
canChangeEncoding(String, String) DefaultLexicalMapper
canChangeEncoding(String, String) LDCPosMapper
canChangeEncoding(String, String) DefaultMapper
canChangeEncoding(String, String) Mapper
Indicates whether child can be converted to another encoding.
CandidatePartOfSpeechAnnotation() ParserAnnotations.CandidatePartOfSpeechAnnotation
CandidatePhrase edu.stanford.nlp.patterns
Created by Sonal Gupta on 11/7/14.
canReach(Configuration, DependencyTree) ArcStandard
capacity() Beam
capitalize(String) StringUtils
Uppercases the first character of a string.
capture(OutputStream) RedwoodConfiguration
Capture a system stream
captureSystemStreams(boolean, boolean) Redwood
Captures System.out and System.err and redirects them
to Redwood logging.
CASE ChineseGrammaticalRelations
The "case" grammatical relation.
CASE_ALLCAPS AceToken
CASE_ALLCAPSORDOTS AceToken
CASE_ALLDIGITS AceToken
CASE_ALLDIGITSORDOTS AceToken
CASE_CAPINI AceToken
CASE_INCAP AceToken
CASE_INSENSITIVE NodePattern
CASE_MARKER UniversalEnglishGrammaticalRelations
The "prepositional modifier" grammatical relation.
CASE_OTHER AceToken
casedDistSim SeqClassifierFlags
Whether to (not) lowercase tokens before looking them up in distsim
lexicon.
CaseExpression(Env) TokenSequenceParser
CaseExpression(List<Pair<Expression, Expression>>, Expression) Expressions.CaseExpression
caseInsensitive PhraseTable
cast(Iterable<?>, Class<? extends T>) Iterables
Casts all values in the given Iterable to the given type.
cast(String, Type) MetaClass
Cast a String representation of an object into that object.
castToInt(double[][]) ArrayMath
castWithoutKnowingType(String) MetaClass
category() CategoryWordTag
category() CoreLabel
Return the category value of the label (or null if none).
category() HasCategory
Return the category value of the label (or null if none).
CATEGORY_FIELD_NUMBER CoreNLPProtos.Token
categoryAndFunction(String) AbstractTreebankLanguagePack
Returns the syntactic category and 'function' of a String.
categoryAndFunction(String) TreebankLanguagePack
Returns the syntactic category and 'function' of a String.
CategoryAnnotation() CoreAnnotations.CategoryAnnotation
CategoryFunctionalTagAnnotation() CoreAnnotations.CategoryFunctionalTagAnnotation
CategoryWordTag edu.stanford.nlp.ling
A CategoryWordTag object acts as a complex Label
which contains a category, a head word, and a tag.
CategoryWordTag() CategoryWordTag
CategoryWordTag(String) CategoryWordTag
This one argument constructor sets just the value.
CategoryWordTag(String, String, String) CategoryWordTag
CategoryWordTag(Label) CategoryWordTag
Creates a new CategoryWordTag label from an existing label.
CategoryWordTagFactory edu.stanford.nlp.ling
A CategoryWordTagFactory is a factory that makes
a Label which is a CategoryWordTag triplet.
CategoryWordTagFactory() CategoryWordTagFactory
CatErrorEval(String) AbstractEval.CatErrorEval
CBEval(String, boolean) Evalb.CBEval
cCommands(Tree, Tree) Tree
Given nodes t1 and t2 which are
dominated by this node, returns true iff
t1 c-commands t2.
ccompObjVerbRegex EnglishPatterns
ccompVerbRegex EnglishPatterns
CCtransform(Tree) CoordinationTransformer
Transforms t if it contains a coordination in a flat structure
CEDict edu.stanford.nlp.trees.international.pennchinese
Centuries JodaTimeUtils
CENTURY SUTime
cf CollectionValuedMap
cf TwoDimensionalCollectionValuedMap
CGMinimizer edu.stanford.nlp.optimization
Conjugate-gradient implementation based on the code in Numerical
Recipes in C.
CGMinimizer() CGMinimizer
Basic constructor, use this.
CGMinimizer(boolean) CGMinimizer
Pass in false to get per-iteration progress reports
(to stderr).
CGMinimizer(Function) CGMinimizer
Perform minimization with monitoring.
CGRunner edu.stanford.nlp.maxent
This class will call an optimization method such as Conjugate Gradient or
Quasi-Newton on a LambdaSolve object to find
optimal parameters, including imposing a Gaussian prior on those
parameters.
CGRunner(LambdaSolve, String) CGRunner
Set up a LambdaSolve problem for solution by a Minimizer.
CGRunner(LambdaSolve, String, double) CGRunner
Set up a LambdaSolve problem for solution by a Minimizer,
specifying a value for sigma2 .
CGRunner(LambdaSolve, String, double, double) CGRunner
Set up a LambdaSolve problem for solution by a Minimizer.
CGRunner(LambdaSolve, String, double, double[]) CGRunner
Set up a LambdaSolve problem for solution by a Minimizer.
chain(Comparator<? super T>, Comparator<? super T>) Comparators
Returns a new Comparator which is the result of chaining the
given Comparators.
chain(List<Comparator<? super T>>) Comparators
Returns a new Comparator which is the result of chaining the
given Comparators.
chain(Comparator<? super T>...) Comparators
chain(Iterable<? extends Iterable<T>>) Iterables
Chains together a set of Iterables of compatible types.
chain(Iterable<T>...) Iterables
Chains together all Iterables of type T as given in an array or
varargs parameter.
chain(T[]...) Iterables
Chains together all arrays of type T[] as given in an array or
varargs parameter.
chain(LogRecordHandler[], RedwoodConfiguration.Thunk) RedwoodConfiguration.Handlers
Apply each of the handlers to incoming log messages, in sequence.
chain(LogRecordHandler, RedwoodConfiguration.Thunk) RedwoodConfiguration.Handlers
chain(LogRecordHandler, LogRecordHandler, RedwoodConfiguration.Thunk) RedwoodConfiguration.Handlers
chain(LogRecordHandler, LogRecordHandler, LogRecordHandler, RedwoodConfiguration.Thunk) RedwoodConfiguration.Handlers
chain(LogRecordHandler, LogRecordHandler, LogRecordHandler, LogRecordHandler, RedwoodConfiguration.Thunk) RedwoodConfiguration.Handlers
chain(LogRecordHandler, LogRecordHandler, LogRecordHandler, LogRecordHandler, LogRecordHandler, RedwoodConfiguration.Thunk) RedwoodConfiguration.Handlers
CHAINID_FIELD_NUMBER CoreNLPProtos.CorefChain
changeFeatureIndex(Index<F>) Dataset
changeLabelIndex(Index<L>) Dataset
changePriority(E, double) BinaryHeapPriorityQueue
Changes a priority, either up or down, adding the key it if it wasn't there already.
changePriority(E, double) FixedPrioritiesPriorityQueue
Not supported in this implementation.
changePriority(E, double) PriorityQueue
Changes a priority, either up or down, adding the key it if it wasn't there already.
changeScore(double) SentenceFragment
Changes the score of this fragment in place.
channelColors OutputHandler
channels(Object...) Redwood
Create an object representing a group of channels.
channels() Redwood.Record
Returns the channels for this record, in sorted order (special channels first, then alphabetical)
channels(Object...) Redwood.RedwoodChannels
Creates a new RedwoodChannels object, concatenating the channels from
this RedwoodChannels with some additional channels.
channels(Object...) Redwood.Util
channelSeparatorChar OutputHandler
Character used to join multiple channel names
channelStyles OutputHandler
channelWidth(int) RedwoodConfiguration
Set the width of the channels (or 0 to not show channels)
characterOffsetBegin() Sentence
characterOffsetBegin(int) Sentence
The character offset of the given index in the sentence.
CHARACTEROFFSETBEGIN_FIELD_NUMBER CoreNLPProtos.Sentence
CharacterOffsetBeginAnnotation() CoreAnnotations.CharacterOffsetBeginAnnotation
characterOffsetEnd() Sentence
characterOffsetEnd(int) Sentence
The end character offset of the given index in the sentence.
CHARACTEROFFSETEND_FIELD_NUMBER CoreNLPProtos.Sentence
CharacterOffsetEndAnnotation() CoreAnnotations.CharacterOffsetEndAnnotation
characters(char[], int, int) TransformXML.SAXInterface
Characters edu.stanford.nlp.util
Character-level utilities.
CharactersAnnotation() ChineseCoreAnnotations.CharactersAnnotation
CharAnnotation() CoreAnnotations.CharAnnotation
charArrToByteArr(char[]) ConvertByteArray
charArrToByteArr(char[], int, int) ConvertByteArray
charArrToByteArr(byte[], int, char[], int, int) ConvertByteArray
charHalfWindow SeqClassifierFlags
CharniakParser edu.stanford.nlp.parser.charniak
Runs charniak parser using command line
CharniakParser() CharniakParser
CharniakParser(String, String) CharniakParser
CharniakParserAnnotator edu.stanford.nlp.pipeline
This class will add parse information to an Annotation from the BLLIP parser.
CharniakParserAnnotator(String, String, boolean, int) CharniakParserAnnotator
CharniakParserAnnotator() CharniakParserAnnotator
CharniakScoredParsesReaderWriter edu.stanford.nlp.parser.charniak
Utility routines for printing/reading scored parses for the Charniak Parser
CharniakScoredParsesReaderWriter() CharniakScoredParsesReaderWriter
charOffsets MatchedExpression
Character offsets (relative to original text).
chart BiLexPCFGParser
charTags ChineseTreebankParserParams
cheatPCFG TrainOptions
Add all test set trees to training data for PCFG.
check() IntervalTree
check(IntervalTree.TreeNode<E, T>) IntervalTree
checkArgs(List<Value>) ValueFunction
Checks if the arguments are valid.
checkArgs(List<Value>) ValueFunctions.BooleanFunction
checkArgs(List<Value>) ValueFunctions.CompareFunction
checkArgs(List<Value>) ValueFunctions.NumericFunction
checkArgs(List<Value>) ValueFunctions.StringFunction
checkArgs(List<Value>) ValueFunctions.TypeCheckedFunction
checkClusters(Logger, String, Document) SieveCoreferenceSystem
checkConstructor(Object...) MetaClass
checkCorrectness() LambdaSolve
Check whether the constraints are satisfied, the probabilities sum to one, etc.
checkCorrectness() LambdaSolveTagger
Works out whether the model expectations match the empirical
expectations.
checkCrossing(Set<Constituent>, Set<Constituent>) Evalb.CBEval
checkDic(String, SeqClassifierFlags) NonDict2
checkEntityMatch(Document, CorefCluster, CorefCluster, Dictionaries, Set<Mention>) DeterministicCorefSieve
checkEntityMatch(Document, CorefCluster, CorefCluster, Dictionaries, Set<Mention>) NameMatch
checkEntityMatch(Document, CorefCluster, CorefCluster, Dictionaries, Set<Mention>) DeterministicCorefSieve
checkEntityMatch(Document, CorefCluster, CorefCluster, Dictionaries, Set<Mention>) NameMatch
checkError() RedwoodPrintStream
checkExistsOrFail(File) FileSystem
checkFile(File) NERGUI
checkFlagExclusiveSet(int, int, int) Interval
Utility function to check if a particular flag is set exclusively
given a particular set of flags and a mask.
checkFlagSet(int, int) Interval
Utility function to check if a particular flag is set
given a particular set of flags.
checkGradient SeqClassifierFlags
checkMemory(Properties) CorefProperties
checkMultipleBitSet(int) Interval
Utility function to check if multiple bits are set for flags
checkNameList SeqClassifierFlags
checkNotExistsOrFail(File) FileSystem
checkOffsets(CoreMap) ChunkAnnotationUtils
Checks if offsets of doc and sentence matches
checkProperties(Properties, Properties) PropertiesUtils
Checks to make sure that all properties specified in properties
are known to the program by checking that each simply overrides
a default value.
checkRequiredProperties(Properties, String...) StringUtils
If any of the given list of properties are not found, returns the
name of that property.
checkTime(Properties) CorefProperties
checkWordListMatch(PhraseTable.Phrase, PhraseTable.WordList, int, int, int, boolean) PhraseTable
child UnaryRule
CHILD_FIELD_NUMBER CoreNLPProtos.ParseTree
childFilteredEval(String, boolean, TreebankLanguagePack, String) FilteredEval
Returns an eval which is good for counting the attachment of
specific node types.
childPairs(IndexedWord) SemanticGraph
Returns a list of pairs of a relation name and the child
IndexedFeatureLabel that bears that relation.
childRelns(IndexedWord) SemanticGraph
Returns a list of relations which this node has with its children.
children TrieMap
Child tries
children() LabeledScoredTreeNode
Returns an array of children for the current node, or null
if it is a leaf.
children() SimpleTree
Returns an array of children for the current node, or null
if it is a leaf.
children() Tree
Returns an array of children for the current node.
children TreeGraphNode
Children of this node.
children() TreeGraphNode
Returns an array of the children of this node.
children() Redwood.RecordHandlerTree
ChildrenAnnotation() TimeExpression.ChildrenAnnotation
childrenNamesSpi() DisabledPreferences
childSpi(String) DisabledPreferences
childTab TreeJPanel.WidthResult
CHINESE FontDetector
Chinese_VALUE CoreNLPProtos.Language
Chinese = 3;
ChineseCharacterBasedLexicon edu.stanford.nlp.parser.lexparser
ChineseCharacterBasedLexicon(ChineseTreebankParserParams, Index<String>, Index<String>) ChineseCharacterBasedLexicon
ChineseCharAnnotation() CoreAnnotations.ChineseCharAnnotation
ChineseCollinizer edu.stanford.nlp.trees.international.pennchinese
Performs collinization operations on Chinese trees similar to
those for English Namely:
strips all functional & automatically-added tags
strips all punctuation
merges PRN and ADVP
eliminates ROOT (note that there are a few non-unary ROOT nodes;
these are not eliminated)
ChineseCollinizer(ChineseTreebankLanguagePack) ChineseCollinizer
ChineseCollinizer(ChineseTreebankLanguagePack, boolean) ChineseCollinizer
chineseColonAcceptFilter() ChineseTreebankLanguagePack
chineseCommaAcceptFilter() ChineseTreebankLanguagePack
ChineseCoreAnnotations edu.stanford.nlp.ling
ChineseCoreAnnotations.CharactersAnnotation edu.stanford.nlp.ling
chineseDashAcceptFilter() ChineseTreebankLanguagePack
ChineseDictionary edu.stanford.nlp.wordseg
This class provides a main method that loads various dictionaries, and
saves them in a serialized version, and runtime compiles them into a word list used as a feature in the segmenter.
ChineseDictionary(String) ChineseDictionary
ChineseDictionary(String[]) ChineseDictionary
ChineseDictionary(String[], ChineseDocumentToSentenceProcessor) ChineseDictionary
ChineseDictionary(String, ChineseDocumentToSentenceProcessor, boolean) ChineseDictionary
The first argument can be one file path, or multiple files separated by
commas.
ChineseDictionary(String[], ChineseDocumentToSentenceProcessor, boolean) ChineseDictionary
ChineseDocumentToSentenceProcessor edu.stanford.nlp.process
Convert a Chinese Document into a List of sentence Strings.
ChineseDocumentToSentenceProcessor() ChineseDocumentToSentenceProcessor
ChineseDocumentToSentenceProcessor(String) ChineseDocumentToSentenceProcessor
chineseDouHaoAcceptFilter() ChineseTreebankLanguagePack
chineseEndSentenceAcceptFilter() ChineseTreebankLanguagePack
ChineseEnglishWordMap edu.stanford.nlp.trees.international.pennchinese
A class for mapping Chinese words to English.
ChineseEnglishWordMap() ChineseEnglishWordMap
Make a ChineseEnglishWordMap with a default CEDict path.
ChineseEnglishWordMap(String) ChineseEnglishWordMap
Make a ChineseEnglishWordMap
ChineseEnglishWordMap(String, boolean) ChineseEnglishWordMap
Make a ChineseEnglishWordMap
ChineseEnglishWordMap(String, String, String, String) ChineseEnglishWordMap
ChineseEnglishWordMap(String, String, String, String, boolean) ChineseEnglishWordMap
ChineseEscaper edu.stanford.nlp.trees.international.pennchinese
An Escaper for Chinese normalization to match Treebank.
ChineseEscaper() ChineseEscaper
ChineseGrammaticalRelations edu.stanford.nlp.trees.international.pennchinese
ChineseGrammaticalStructure edu.stanford.nlp.trees.international.pennchinese
A GrammaticalStructure for Chinese.
ChineseGrammaticalStructure(Tree) ChineseGrammaticalStructure
Construct a new GrammaticalStructure from an
existing parse tree.
ChineseGrammaticalStructure(Tree, Predicate<String>) ChineseGrammaticalStructure
ChineseGrammaticalStructure(Tree, HeadFinder) ChineseGrammaticalStructure
ChineseGrammaticalStructure(Tree, Predicate<String>, HeadFinder) ChineseGrammaticalStructure
ChineseGrammaticalStructure(List<TypedDependency>, TreeGraphNode) ChineseGrammaticalStructure
Used for postprocessing CoNLL X dependencies
ChineseGrammaticalStructure.FromDependenciesFactory edu.stanford.nlp.trees.international.pennchinese
ChineseGrammaticalStructureFactory edu.stanford.nlp.trees.international.pennchinese
ChineseGrammaticalStructureFactory() ChineseGrammaticalStructureFactory
ChineseGrammaticalStructureFactory(Predicate<String>) ChineseGrammaticalStructureFactory
ChineseGrammaticalStructureFactory(Predicate<String>, HeadFinder) ChineseGrammaticalStructureFactory
ChineseHeadFinder edu.stanford.nlp.trees.international.pennchinese
HeadFinder for the Penn Chinese Treebank.
ChineseHeadFinder() ChineseHeadFinder
ChineseHeadFinder(TreebankLanguagePack) ChineseHeadFinder
ChineseHeadMatch edu.stanford.nlp.hcoref.sieve
ChineseHeadMatch() ChineseHeadMatch
ChineseHeadMatch(Properties) ChineseHeadMatch
ChineseIsSegmentedAnnotation() CoreAnnotations.ChineseIsSegmentedAnnotation
chineseLeftParenthesisAcceptFilter() ChineseTreebankLanguagePack
chineseLeftQuoteMarkAcceptFilter() ChineseTreebankLanguagePack
ChineseLexicon edu.stanford.nlp.parser.lexparser
A lexicon class for Chinese.
ChineseLexicon(Options, ChineseTreebankParserParams, Index<String>, Index<String>) ChineseLexicon
ChineseLexiconAndWordSegmenter edu.stanford.nlp.parser.lexparser
This class lets you train a lexicon and segmenter at the same time.
ChineseLexiconAndWordSegmenter(ChineseLexicon, WordSegmenter) ChineseLexiconAndWordSegmenter
ChineseLexiconAndWordSegmenter(String, Options) ChineseLexiconAndWordSegmenter
Construct a new ChineseLexiconAndWordSegmenter.
ChineseMarkovWordSegmenter edu.stanford.nlp.parser.lexparser
Performs word segmentation with a hierarchical markov model over POS
and over characters given POS.
ChineseMarkovWordSegmenter(ChineseCharacterBasedLexicon, Index<String>, Index<String>) ChineseMarkovWordSegmenter
ChineseMarkovWordSegmenter(ChineseTreebankParserParams, Index<String>, Index<String>) ChineseMarkovWordSegmenter
ChineseOrigSegAnnotation() CoreAnnotations.ChineseOrigSegAnnotation
chineseOtherAcceptFilter() ChineseTreebankLanguagePack
chineseParenthesisAcceptFilter() ChineseTreebankLanguagePack
chineseQuoteMarkAcceptFilter() ChineseTreebankLanguagePack
chineseRightParenthesisAcceptFilter() ChineseTreebankLanguagePack
chineseRightQuoteMarkAcceptFilter() ChineseTreebankLanguagePack
ChineseSegAnnotation() CoreAnnotations.ChineseSegAnnotation
ChineseSegmenterAnnotator edu.stanford.nlp.pipeline
This class will add segmentation information to an Annotation.
ChineseSegmenterAnnotator() ChineseSegmenterAnnotator
ChineseSegmenterAnnotator(boolean) ChineseSegmenterAnnotator
ChineseSegmenterAnnotator(String, boolean) ChineseSegmenterAnnotator
ChineseSegmenterAnnotator(String, boolean, String, String) ChineseSegmenterAnnotator
ChineseSegmenterAnnotator(String, Properties) ChineseSegmenterAnnotator
ChineseSegmenterFeatureFactory IN extends CoreLabel > - Class in edu.stanford.nlp.wordseg
A Chinese segmenter Feature Factory for GALE project.
ChineseSegmenterFeatureFactory() ChineseSegmenterFeatureFactory
chineseSelectiveTagPA ChineseTreebankParserParams
ChineseSemanticHeadFinder edu.stanford.nlp.trees.international.pennchinese
Implements a 'semantic head' variant of the the HeadFinder found in Chinese Head Finder
ChineseSemanticHeadFinder() ChineseSemanticHeadFinder
ChineseSemanticHeadFinder(TreebankLanguagePack) ChineseSemanticHeadFinder
ChineseSimWordAvgDepGrammar edu.stanford.nlp.parser.lexparser
A Dependency grammar that smooths by averaging over similar words.
ChineseSimWordAvgDepGrammar(TreebankLangParserParams, boolean, boolean, boolean, boolean, Options, Index<String>, Index<String>) ChineseSimWordAvgDepGrammar
chineseSplitDouHao ChineseTreebankParserParams
Chinese: Split the dou hao (a punctuation mark separating
members of a list) from other punctuation.
chineseSplitPunct ChineseTreebankParserParams
Chinese: split Chinese punctuation several ways, along the lines
of English punctuation plus another category for the dou hao.
chineseSplitPunctLR ChineseTreebankParserParams
Chinese: split left right/paren quote (if chineseSplitPunct is also
true.
chineseSplitVP ChineseTreebankParserParams
Chinese VP splitting.
ChineseStringUtils edu.stanford.nlp.wordseg
ChineseTreebankLanguagePack edu.stanford.nlp.trees.international.pennchinese
Language pack for the UPenn/Colorado Chinese treebank.
ChineseTreebankLanguagePack() ChineseTreebankLanguagePack
ChineseTreebankParserParams edu.stanford.nlp.parser.lexparser
Parameter file for parsing the Penn Chinese Treebank.
ChineseTreebankParserParams() ChineseTreebankParserParams
ChineseUnknownWordModel edu.stanford.nlp.parser.lexparser
Stores, trains, and scores with an unknown word model.
ChineseUnknownWordModel(Options, Lexicon, Index<String>, Index<String>, ClassicCounter<IntTaggedWord>, Map<Label, ClassicCounter<String>>, Map<String, Float>, boolean, Set<String>) ChineseUnknownWordModel
ChineseUnknownWordModel(Options, Lexicon, Index<String>, Index<String>) ChineseUnknownWordModel
This constructor creates an UWM with empty data structures.
ChineseUnknownWordModelTrainer edu.stanford.nlp.parser.lexparser
ChineseUnknownWordModelTrainer() ChineseUnknownWordModelTrainer
ChineseUtils edu.stanford.nlp.trees.international.pennchinese
This class contains a few String constants and
static methods for dealing with Chinese text.
chineseVerySelectiveTagPA ChineseTreebankParserParams
chiSquare2by2(int, int, int, int) SloppyMath
Find a 2x2 chi-square value.
chomp(String) StringUtils
Returns the supplied string with any trailing '\n' removed.
chomp(Object) StringUtils
Returns the result of calling toString() on the supplied Object, but with
any trailing '\n' removed.
chooseTokenizerFactory() MaxentTagger
Figures out what tokenizer factory might be described by the
config.
chooseTokenizerFactory(boolean, String, String, boolean) MaxentTagger
chooseTopWords(Counter<CandidatePhrase>, TwoDimensionalCounter<CandidatePhrase, E>, Counter<CandidatePhrase>, Set<CandidatePhrase>, double) ScorePhrases
CHTBTokenizer edu.stanford.nlp.trees.international.pennchinese
A simple tokenizer for tokenizing Penn Chinese Treebank files.
CHTBTokenizer(Reader) CHTBTokenizer
Constructs a new tokenizer from a Reader.
ChunkAnnotation() CoreAnnotations.ChunkAnnotation
ChunkAnnotationUtils edu.stanford.nlp.pipeline
Utility functions for annotating chunks
ChunkAnnotationUtils() ChunkAnnotationUtils
chunkOffsets MatchedExpression
Chunk offsets (relative to chunking on top of original text)
CL NaiveBayesClassifierFactory
classBias SeqClassifierFlags
ClassComparator() Token.ClassComparator
ClassicCounter E > - Class in edu.stanford.nlp.stats
A specialized kind of hash table (or map) for storing numeric counts for
objects.
ClassicCounter() ClassicCounter
Constructs a new (empty) Counter backed by a HashMap.
ClassicCounter(int) ClassicCounter
ClassicCounter(MapFactory<E, MutableDouble>) ClassicCounter
Pass in a MapFactory and the map it vends will back your Counter.
ClassicCounter(MapFactory<E, MutableDouble>, int) ClassicCounter
Pass in a MapFactory and the map it vends will back your Counter.
ClassicCounter(Counter<E>) ClassicCounter
Constructs a new Counter with the contents of the given Counter.
ClassicCounter(Collection<E>) ClassicCounter
Constructs a new Counter by counting the elements in the given Collection.
CLASSIFICATION_MODEL StatisticalCorefTrainer
classificationModelPath(Properties) StatisticalCorefProperties
classificationScores ClustererDataLoader.ClustererDoc
Classifier L ,F > - Interface in edu.stanford.nlp.classify
A simple interface for classifying and scoring data points, implemented
by most of the classifiers in this package.
classifier BasicRelationExtractor
Classifier edu.stanford.nlp.parser.nndep
Neural network classifier which powers a transition-based dependency
parser.
Classifier(Config, double[][], double[][], double[], double[][], List<Integer>) Classifier
Instantiate a classifier with previously learned parameters in
order to perform new inference.
Classifier(Config, Dataset, double[][], double[][], double[], double[][], List<Integer>) Classifier
Instantiate a classifier with training data and randomly
initialized parameter matrices in order to begin training.
classifier PairwiseModel
Classifier.Cost edu.stanford.nlp.parser.nndep
Describes the result of feedforward + backpropagation through
the neural network for the batch provided to a `CostFunction.`
CLASSIFIER_MODIFIER ChineseGrammaticalRelations
The "classifier modifier" grammatical relation.
CLASSIFIER_TYPE_PROP CorefProperties
ClassifierCombiner IN extends CoreMap & HasWord > - Class in edu.stanford.nlp.ie
Merges the outputs of two or more AbstractSequenceClassifiers according to
a simple precedence scheme: any given base classifier contributes only
classifications of labels that do not exist in the base classifiers specified
before, and that do not have any token overlap with labels assigned by
higher priority classifiers.
ClassifierCombiner(Properties) ClassifierCombiner
ClassifierCombiner(Properties, ClassifierCombiner.CombinationMode, String...) ClassifierCombiner
Loads a series of base classifiers from the paths specified using the
Properties specified.
ClassifierCombiner(ClassifierCombiner.CombinationMode, String...) ClassifierCombiner
Loads a series of base classifiers from the paths specified using the
Properties specified.
ClassifierCombiner(String...) ClassifierCombiner
Loads a series of base classifiers from the paths specified.
ClassifierCombiner(AbstractSequenceClassifier<IN>...) ClassifierCombiner
Combines a series of base classifiers.
ClassifierCombiner(ObjectInputStream, Properties) ClassifierCombiner
ClassifierCreator L ,F > - Interface in edu.stanford.nlp.classify
Creates a classifier with given weights
ClassifierExample edu.stanford.nlp.classify
Sample code that illustrates the training and use of a linear classifier.
ClassifierFactory L ,F ,C extends Classifier <L ,F >> - Interface in edu.stanford.nlp.classify
A simple interface for training a Classifier from a Dataset of training
examples.
classifierFactory ClauseSplitterSearchProblem.TrainingOptions
classifierType Sieve
classifierType SeqClassifierFlags
classify(String) AbstractSequenceClassifier
Classify the tokens in a String.
classify(List<IN>) AbstractSequenceClassifier
classify(List<IN>) ClassifierCombiner
Generates the AnswerAnnotation labels of the combined model for the given
tokens, storing them in place in the tokens.
classify(List<IN>) CRFBiasedClassifier
classify(List<IN>) CRFClassifier
classify(List<IN>) CMMClassifier
classify(List<CoreLabel>) NERClassifierCombiner
classify(List<CoreLabel>) NumberSequenceClassifier
classify(List<CoreLabel>) RegexNERSequenceClassifier
classifyAndWriteAnswers(String) AbstractSequenceClassifier
Load a test file, run the classifier on it, and then print the answers to
stdout (with timing to stderr).
classifyAndWriteAnswers(String, DocumentReaderAndWriter<IN>, boolean) AbstractSequenceClassifier
Load a test file, run the classifier on it, and then print the answers to
stdout (with timing to stderr).
classifyAndWriteAnswers(String, OutputStream, DocumentReaderAndWriter<IN>, boolean) AbstractSequenceClassifier
If the flag
outputEncoding is defined, the output is written in that
character encoding, otherwise in the system default character encoding.
classifyAndWriteAnswers(String, String, DocumentReaderAndWriter<IN>, boolean) AbstractSequenceClassifier
classifyAndWriteAnswers(Collection<List<IN>>, DocumentReaderAndWriter<IN>, boolean) AbstractSequenceClassifier
classifyAndWriteAnswers(Collection<List<IN>>, PrintWriter, DocumentReaderAndWriter<IN>, boolean) AbstractSequenceClassifier
classifyAndWriteAnswersKBest(String, int, DocumentReaderAndWriter<IN>) AbstractSequenceClassifier
Load a test file, run the classifier on it, and then print the answers to
stdout (with timing to stderr).
classifyAndWriteAnswersKBest(ObjectBank<List<IN>>, int, PrintWriter, DocumentReaderAndWriter<IN>) AbstractSequenceClassifier
Run the classifier on the documents in an ObjectBank, and print the
answers to a given PrintWriter (with timing to stderr).
classifyAndWriteViterbiSearchGraph(String, String, DocumentReaderAndWriter<IN>) AbstractSequenceClassifier
Load a test file, run the classifier on it, and then write a Viterbi search
graph for each sequence.
classifyFile(String) AbstractSequenceClassifier
Classify the contents of a file.
classifyFilesAndWriteAnswers(Collection<File>) AbstractSequenceClassifier
classifyFilesAndWriteAnswers(Collection<File>, DocumentReaderAndWriter<IN>, boolean) AbstractSequenceClassifier
classifyGibbs(List<IN>) CRFClassifier
classifyGibbs(List<IN>, Triple<int[][][], int[], double[][][]>) CRFClassifier
classifyKBest(List<IN>, Class<? extends CoreAnnotation<String>>, int) AbstractSequenceClassifier
Takes a list of tokens and provides the K best sequence labelings of these tokens with their scores.
classifyMaxEnt(List<IN>) CRFClassifier
Do standard sequence inference, using either Viterbi or Beam inference
depending on the value of flags.inferenceType.
classifyMentions(List<List<Mention>>, Dictionaries, Properties) MentionDetectionClassifier
classifyRaw(String, DocumentReaderAndWriter<IN>) AbstractSequenceClassifier
Classify the tokens in a String.
classifySentence(List<? extends HasWord>) AbstractSequenceClassifier
Classify a List of IN.
classifySentenceWithGlobalInformation(List<? extends HasWord>, CoreMap, CoreMap) AbstractSequenceClassifier
Classify a List of IN using whatever additional information is passed in globalInfo.
classifyStdin() AbstractSequenceClassifier
classifyStdin(DocumentReaderAndWriter<IN>) AbstractSequenceClassifier
classifyToCharacterOffsets(String) AbstractSequenceClassifier
Classify the contents of a
String to classified character offset
spans.
classifyToString(String, String, boolean) AbstractSequenceClassifier
Classify the contents of a
String to one of several String
representations that shows the classes.
classifyToString(String) AbstractSequenceClassifier
Classify the contents of a String to a tagged word/class String.
classifyWithGlobalInformation(List<IN>, CoreMap, CoreMap) AbstractSequenceClassifier
Classify a
List of something that extends
CoreMap using as
additional information whatever is stored in the document and sentence.
classifyWithGlobalInformation(List<IN>, CoreMap, CoreMap) ClassifierCombiner
classifyWithGlobalInformation(List<IN>, CoreMap, CoreMap) CRFClassifier
classifyWithGlobalInformation(List<IN>, CoreMap, CoreMap) CMMClassifier
classifyWithGlobalInformation(List<CoreLabel>, CoreMap, CoreMap) NERClassifierCombiner
classifyWithGlobalInformation(List<CoreLabel>, CoreMap, CoreMap) NumberSequenceClassifier
classifyWithGlobalInformation(List<CoreLabel>, CoreMap, CoreMap) RegexNERSequenceClassifier
classifyWithInlineXML(String) AbstractSequenceClassifier
Classify the contents of a
String.
classIndex AbstractSequenceClassifier
classIndex() CRFCliqueTree
classIndex CRFLogConditionalObjectiveFunction
classIndex EntityCachingAbstractSequencePriorBIO
className ValueFunctions.ParamInfo
classNames RNNOptions
classOf(Datum<L, F>) Classifier
classOf(Datum<String, String>) ColumnDataClassifier
classOf(Datum<L, F>) LinearClassifier
classOf(RVFDatum<L, F>) LinearClassifier
Deprecated.
classOf(Datum<L, F>) LogisticClassifier
classOf(RVFDatum<L, F>) LogisticClassifier
Deprecated.
classOf(Counter<F>) LogisticClassifier
classOf(Collection<F>) LogisticClassifier
classOf(RVFDatum<L, F>) NaiveBayesClassifier
classOf(Datum<L, F>) NaiveBayesClassifier
classOf(RVFDatum<L, F>) RVFClassifier
classOf(Datum<String, String>, ExtractionObject) BasicRelationExtractor
classOf(List<IN>, int) CMMClassifier
Returns the most likely class for the word at the given position.
classORRestrictionsAsString() Token
CLAUSAL_COMPLEMENT EnglishGrammaticalRelations
The "clausal complement" grammatical relation.
CLAUSAL_COMPLEMENT ChineseGrammaticalRelations
The "clausal complement" (ccomp) grammatical relation.
CLAUSAL_COMPLEMENT UniversalEnglishGrammaticalRelations
The "clausal complement" grammatical relation.
CLAUSAL_MODIFIER UniversalEnglishGrammaticalRelations
The "clausal modifier of noun" relation.
CLAUSAL_PASSIVE_SUBJECT EnglishGrammaticalRelations
The "clausal passive subject" grammatical relation.
CLAUSAL_PASSIVE_SUBJECT UniversalEnglishGrammaticalRelations
The "clausal passive subject" grammatical relation.
CLAUSAL_SUBJECT EnglishGrammaticalRelations
The "clausal subject" grammatical relation.
CLAUSAL_SUBJECT UniversalEnglishGrammaticalRelations
The "clausal subject" grammatical relation.
clausalComplementRegex EnglishPatterns
clauseIntermWeight ClauseSplitterSearchProblem.TrainingOptions
clauseRelations UniversalEnglishGrammaticalRelations
clausesInSentence(SemanticGraph, boolean) OpenIE
Find the clauses in a sentence, where the sentence is expressed as a dependency tree.
clausesInSentence(CoreMap) OpenIE
Find the clauses in a sentence.
ClauseSplitter edu.stanford.nlp.naturalli
Just a convenience alias for a clause splitting search problem factory.
clauseSplitter OpenIE
The clause splitter model, if one is to be used.
ClauseSplitter.ClauseClassifierLabel edu.stanford.nlp.naturalli
ClauseSplitterSearchProblem edu.stanford.nlp.naturalli
A search problem for finding clauses in a sentence.
ClauseSplitterSearchProblem(SemanticGraph, boolean, Optional<Classifier<ClauseSplitter.ClauseClassifierLabel, String>>, Optional<Function<Triple<ClauseSplitterSearchProblem.State, ClauseSplitterSearchProblem.Action, ClauseSplitterSearchProblem.State>, Counter<String>>>) ClauseSplitterSearchProblem
Create a searcher manually, suppling a dependency tree, an optional classifier for when to split clauses,
and a featurizer for that classifier.
ClauseSplitterSearchProblem(SemanticGraph, boolean) ClauseSplitterSearchProblem
Create a clause searcher which searches naively through every possible subtree as a clause.
ClauseSplitterSearchProblem.Action edu.stanford.nlp.naturalli
An action being taken; that is, the type of clause splitting going on.
ClauseSplitterSearchProblem.Featurizer edu.stanford.nlp.naturalli
Mostly just an alias, but make sure our featurizer is serializable!
ClauseSplitterSearchProblem.State edu.stanford.nlp.naturalli
A search state.
ClauseSplitterSearchProblem.TrainingOptions edu.stanford.nlp.naturalli
The options used for training the clause searcher.
clauseSplitWeight ClauseSplitterSearchProblem.TrainingOptions
clean() Features
clean() TwoDimensionalCounter
clean() TwoDimensionalIntCounter
CLEAN_XML_REQUIREMENT Annotator
cleanGazette SeqClassifierFlags
cleanJSON(String) JSONOutputter
cleanTree(SemanticGraph) Util
Fix some bizarre peculiarities with certain trees.
cleanUpLabel(String) BobChrisTreeNormalizer
Remove things like hyphened functional tags and equals from the
end of a node label.
cleanUpLabel(String) DependencyTreeTransformer
cleanUpLabel(String) GrammaticalFunctionTreeNormalizer
Remove things like hyphened functional tags and equals from the
end of a node label.
cleanUpLabel(String) NegraPennTreeNormalizer
Remove things like hyphened functional tags and equals from the
end of a node label.
cleanUpLabel(String) CTBErrorCorrectingTreeNormalizer
Remove things like hyphened functional tags and equals from the
end of a node label.
cleanUpLabel(String) TueBaDZPennTreeNormalizer
Remove things like hyphened functional tags and equals from the
end of a node label.
cleanUpLabel(String) NPTmpRetainingTreeNormalizer
Remove things like hyphened functional tags and equals from the
end of a node label.
cleanUpRoot(String) DependencyTreeTransformer
cleanUpScorer() TestSentence
clean-up after the scorer
cleanXML(Properties, AnnotatorImplementations) AnnotatorFactories
cleanXML(Properties, String, String, String, boolean) AnnotatorImplementations
Clean XML input
CleanXmlAnnotator edu.stanford.nlp.pipeline
An annotator which removes all XML tags (as identified by the
tokenizer) and possibly selectively keeps the text between them.
CleanXmlAnnotator() CleanXmlAnnotator
CleanXmlAnnotator(String, String, String, boolean) CleanXmlAnnotator
clear() GeneralDataset
Resets the Dataset so that it is empty and ready to collect data.
clear(int) GeneralDataset
Resets the Dataset so that it is empty and ready to collect data.
clear() RVFDataset
Resets the Dataset so that it is empty and ready to collect data.
clear(int) RVFDataset
Resets the Dataset so that it is empty and ready to collect data.
clear() DirectedMultiGraph
clears the graph, removes all edges and nodes
clear() Graph
clears the graph, removes all edges and nodes
clear() StringDictionary
clear() StringOutputStream
clear() BoundedCostOrderedMap
clear() TrieMap
clear() PhraseTable
Clears this table
clear() PhraseTable.PhraseStringCollection
clear() ObjectBank
clear() QNMinimizer.QNInfo
clear() CoreNLPProtos.CorefChain.Builder
clear() CoreNLPProtos.CorefChain.CorefMention.Builder
clear() CoreNLPProtos.DependencyGraph.Builder
clear() CoreNLPProtos.DependencyGraph.Edge.Builder
clear() CoreNLPProtos.DependencyGraph.Node.Builder
clear() CoreNLPProtos.Document.Builder
clear() CoreNLPProtos.Entity.Builder
clear() CoreNLPProtos.IndexedWord.Builder
clear() CoreNLPProtos.MapIntString.Builder
clear() CoreNLPProtos.MapStringString.Builder
clear() CoreNLPProtos.Mention.Builder
clear() CoreNLPProtos.NERMention.Builder
clear() CoreNLPProtos.OpenIETriple.Builder
clear() CoreNLPProtos.Operator.Builder
clear() CoreNLPProtos.ParseTree.Builder
clear() CoreNLPProtos.Polarity.Builder
clear() CoreNLPProtos.Quote.Builder
clear() CoreNLPProtos.Relation.Builder
clear() CoreNLPProtos.Sentence.Builder
clear() CoreNLPProtos.SentenceFragment.Builder
clear() CoreNLPProtos.Span.Builder
clear() CoreNLPProtos.SpeakerInfo.Builder
clear() CoreNLPProtos.Timex.Builder
clear() CoreNLPProtos.Token.Builder
clear() ObjectBankWrapper
clear() ClassicCounter
Removes all entries from the counter.
clear() Counter
Removes all entries from the counter.
clear() IntCounter
Removes all counts from this Counter.
clear() TwoDimensionalCounter
clears the map, total and default value
clear() SUTime.TimeIndex
clear() CompositeTreebank
clear() DiskTreebank
Empty a Treebank.
clear() FilteringTreebank
Empty a Treebank.
clear() MemoryTreebank
Empty a Treebank.
clear() TransformingTreebank
Empty a Treebank.
clear() Treebank
Empty a Treebank.
clear() ArrayHeap
Clears the heap.
clear() ArrayMap
clear() BinaryHeapPriorityQueue
Clears the queue.
clear() CollectionValuedMap
Clears this Map.
clear() ConcurrentHashIndex
clear() SynchronizedInterner
clear() DeltaCollectionValuedMap
This is more expensive than normal.
clear() DeltaIndex
We don't want to change the backing index in any way, and "clear"
would have to entail doing that, so we just throw an
UnsupportedOperationException instead
clear() DeltaMap
This is more expensive than normal.
clear(int, int) EditDistance
clear() FixedPrioritiesPriorityQueue
clear() HashIndex
Clears this Index.
clear() IdentityHashSet
Removes all of the elements from this set.
clear() Index
clear() Interner
clear() IntervalTree
clear() IntervalTree.TreeNode
clear() RedwoodConfiguration
Clear any custom configurations to Redwood
clear() MaxSizeConcurrentHashSet
clear() TwoDimensionalCollectionValuedMap
clear() TwoDimensionalMap
clear() TwoDimensionalSet
clear2D(double[][]) CRFLogConditionalObjectiveFunction
clearAfter() CoreNLPProtos.Token.Builder
optional string after = 6;
clearAll() FilePanel
Removes all files from the panel
clearAlternativeDependencies() CoreNLPProtos.Sentence.Builder
optional .edu.stanford.nlp.pipeline.DependencyGraph alternativeDependencies = 13;
clearAltValue() CoreNLPProtos.Timex.Builder
optional string altValue = 2;
clearAnimacy() CoreNLPProtos.CorefChain.CorefMention.Builder
optional string animacy = 5;
clearAnimacy() CoreNLPProtos.Mention.Builder
optional string animacy = 5;
clearAnnotatedParseTree() CoreNLPProtos.Sentence.Builder
optional .edu.stanford.nlp.pipeline.ParseTree annotatedParseTree = 32;
clearAnnotatorPool() StanfordCoreNLP
Call this if you are no longer using StanfordCoreNLP and want to
release the memory associated with the annotators.
clearAnswer() CoreNLPProtos.Token.Builder
optional string answer = 24;
clearAppositions() CoreNLPProtos.Mention.Builder
repeated int32 appositions = 54;
clearArg() CoreNLPProtos.Relation.Builder
repeated .edu.stanford.nlp.pipeline.Entity arg = 7;
clearArgName() CoreNLPProtos.Relation.Builder
repeated string argName = 6;
clearAssumedTruth() CoreNLPProtos.SentenceFragment.Builder
optional bool assumedTruth = 3;
clearBasicDependencies() CoreNLPProtos.Sentence.Builder
optional .edu.stanford.nlp.pipeline.DependencyGraph basicDependencies = 8;
clearBefore() CoreNLPProtos.Token.Builder
optional string before = 5;
clearBegin() CoreNLPProtos.Quote.Builder
optional uint32 begin = 2;
clearBegin() CoreNLPProtos.Span.Builder
required uint32 begin = 1;
clearBeginChar() CoreNLPProtos.Token.Builder
optional uint32 beginChar = 11;
clearBeginIndex() CoreNLPProtos.CorefChain.CorefMention.Builder
optional uint32 beginIndex = 6;
clearBeginIndex() CoreNLPProtos.Token.Builder
optional uint32 beginIndex = 15;
clearBeginPoint() CoreNLPProtos.Timex.Builder
optional uint32 beginPoint = 6;
clearBelongToLists() CoreNLPProtos.Mention.Builder
repeated int32 belongToLists = 58;
clearBinarizedParseTree() CoreNLPProtos.Sentence.Builder
optional .edu.stanford.nlp.pipeline.ParseTree binarizedParseTree = 31;
clearCache() AbstractCachingDiffFunction
Clears the cache in a way that doesn't require reallocation :-)
clearCache() AbstractStochasticCachingDiffFunction
Clears the cache in a way that doesn't require reallocation :-)
clearCategory() CoreNLPProtos.Token.Builder
optional string category = 4;
clearChainID() CoreNLPProtos.CorefChain.Builder
required int32 chainID = 1;
clearCharacterOffsetBegin() CoreNLPProtos.Sentence.Builder
optional uint32 characterOffsetBegin = 5;
clearCharacterOffsetEnd() CoreNLPProtos.Sentence.Builder
optional uint32 characterOffsetEnd = 6;
clearChild() CoreNLPProtos.ParseTree.Builder
repeated .edu.stanford.nlp.pipeline.ParseTree child = 1;
clearCoarseTag() CoreNLPProtos.Token.Builder
optional string coarseTag = 33;
clearCollapsedCCProcessedDependencies() CoreNLPProtos.Sentence.Builder
optional .edu.stanford.nlp.pipeline.DependencyGraph collapsedCCProcessedDependencies = 10;
clearCollapsedDependencies() CoreNLPProtos.Sentence.Builder
optional .edu.stanford.nlp.pipeline.DependencyGraph collapsedDependencies = 9;
clearConfidence() CoreNLPProtos.OpenIETriple.Builder
optional double confidence = 4;
clearConllUFeatures() CoreNLPProtos.Token.Builder
optional .edu.stanford.nlp.pipeline.MapStringString conllUFeatures = 32;
clearConllUMisc() CoreNLPProtos.Token.Builder
optional string conllUMisc = 35;
clearConllUSecondaryDeps() CoreNLPProtos.Token.Builder
optional .edu.stanford.nlp.pipeline.MapIntString conllUSecondaryDeps = 36;
clearConllUTokenSpan() CoreNLPProtos.Token.Builder
optional .edu.stanford.nlp.pipeline.Span conllUTokenSpan = 34;
clearCopyAnnotation() CoreNLPProtos.DependencyGraph.Node.Builder
optional uint32 copyAnnotation = 3;
clearCopyCount() CoreNLPProtos.IndexedWord.Builder
optional uint32 copyCount = 4;
clearCorefChain() CoreNLPProtos.Document.Builder
repeated .edu.stanford.nlp.pipeline.CorefChain corefChain = 3;
clearCorefClusterID() CoreNLPProtos.Mention.Builder
optional int32 corefClusterID = 15;
clearCorefClusterID() CoreNLPProtos.Token.Builder
optional uint32 corefClusterID = 23;
clearCorefID() CoreNLPProtos.Entity.Builder
optional string corefID = 11;
clearCounts() MultiClassChunkEvalStats
clearCounts() MultiClassPrecisionRecallExtendedStats
clearDep() CoreNLPProtos.DependencyGraph.Edge.Builder
optional string dep = 3;
clearDependents() CoreNLPProtos.Mention.Builder
repeated string dependents = 52;
clearDependingVerb() CoreNLPProtos.Mention.Builder
optional .edu.stanford.nlp.pipeline.IndexedWord dependingVerb = 31;
clearDocID() CoreNLPProtos.Document.Builder
optional string docID = 4;
clearDocID() CoreNLPProtos.IndexedWord.Builder
optional uint32 docID = 3;
clearDocid() CoreNLPProtos.Quote.Builder
optional string docid = 9;
clearDocument() NERGUI
clearEdge() CoreNLPProtos.DependencyGraph.Builder
repeated .edu.stanford.nlp.pipeline.DependencyGraph.Edge edge = 2;
clearEnd() CoreNLPProtos.Quote.Builder
optional uint32 end = 3;
clearEnd() CoreNLPProtos.Span.Builder
required uint32 end = 2;
clearEndChar() CoreNLPProtos.Token.Builder
optional uint32 endChar = 12;
clearEndIndex() CoreNLPProtos.CorefChain.CorefMention.Builder
optional uint32 endIndex = 7;
clearEndIndex() CoreNLPProtos.Mention.Builder
optional uint32 endIndex = 9;
clearEndIndex() CoreNLPProtos.Token.Builder
optional uint32 endIndex = 16;
clearEndPoint() CoreNLPProtos.Timex.Builder
optional uint32 endPoint = 7;
clearEntailedSentence() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.SentenceFragment entailedSentence = 15;
clearEntity() CoreNLPProtos.Sentence.Builder
repeated .edu.stanford.nlp.pipeline.Entity entity = 52;
clearEntityType() CoreNLPProtos.NERMention.Builder
optional string entityType = 6;
clearError() RedwoodPrintStream
clearExtentEnd() CoreNLPProtos.Entity.Builder
optional uint32 extentEnd = 3;
clearExtentEnd() CoreNLPProtos.Relation.Builder
optional uint32 extentEnd = 3;
clearExtentStart() CoreNLPProtos.Entity.Builder
optional uint32 extentStart = 2;
clearExtentStart() CoreNLPProtos.Relation.Builder
optional uint32 extentStart = 2;
clearGender() CoreNLPProtos.CorefChain.CorefMention.Builder
optional string gender = 4;
clearGender() CoreNLPProtos.Mention.Builder
optional string gender = 4;
clearGender() CoreNLPProtos.Token.Builder
optional string gender = 51;
clearGeneric() CoreNLPProtos.Mention.Builder
optional bool generic = 25;
clearGoldCorefClusterID() CoreNLPProtos.Mention.Builder
optional int32 goldCorefClusterID = 14;
clearGradientHistories() Classifier
Clear all gradient histories used for AdaGrad training.
clearGradientsPerIter Config
During training, clear AdaGrad gradient histories after every
clearGradientsPerIter iterations.
clearHandlers() Redwood
Remove all log handlers from Redwood, presumably in order to
construct a custom pipeline afterwards
clearHasBasicDependency() CoreNLPProtos.Mention.Builder
optional bool hasBasicDependency = 27;
clearHasCollapsedDependency() CoreNLPProtos.Mention.Builder
optional bool hasCollapsedDependency = 28;
clearHasContextParseTree() CoreNLPProtos.Mention.Builder
optional bool hasContextParseTree = 29;
clearHasCorefMentionsAnnotation() CoreNLPProtos.Sentence.Builder
optional bool hasCorefMentionsAnnotation = 57;
clearHasNumerizedTokensAnnotation() CoreNLPProtos.Sentence.Builder
optional bool hasNumerizedTokensAnnotation = 54;
clearHasRelationAnnotations() CoreNLPProtos.Sentence.Builder
optional bool hasRelationAnnotations = 51;
clearHasTwin() CoreNLPProtos.Mention.Builder
optional bool hasTwin = 24;
clearHasXmlContext() CoreNLPProtos.Token.Builder
optional bool hasXmlContext = 21;
clearHeadEnd() CoreNLPProtos.Entity.Builder
optional uint32 headEnd = 7;
clearHeadIndex() CoreNLPProtos.CorefChain.CorefMention.Builder
optional uint32 headIndex = 9;
clearHeadIndex() CoreNLPProtos.Mention.Builder
optional uint32 headIndex = 10;
clearHeadIndexedWord() CoreNLPProtos.Mention.Builder
optional .edu.stanford.nlp.pipeline.IndexedWord headIndexedWord = 30;
clearHeadStart() CoreNLPProtos.Entity.Builder
optional uint32 headStart = 6;
clearHeadString() CoreNLPProtos.Mention.Builder
optional string headString = 11;
clearHeadTokenIndex() CoreNLPProtos.Entity.Builder
optional uint32 headTokenIndex = 10;
clearHeadWord() CoreNLPProtos.Mention.Builder
optional .edu.stanford.nlp.pipeline.IndexedWord headWord = 32;
clearHeadWordIndex() CoreNLPProtos.Token.Builder
optional uint32 headWordIndex = 26;
clearIndex()