abc.ABCnltk.tokenize.api.TokenizerI - A processing interface for tokenizing a string. Subclasses must define tokenize() or tokenize_sents() (or both).nltk.parse.corenlp.GenericCoreNLPParser - Interface to the CoreNLP Parser.nltk.parse.corenlp.CoreNLPDependencyParser - Dependency parser.nltk.parse.corenlp.CoreNLPParser - No summarynltk.tokenize.api.StringTokenizer - A tokenizer that divides a string into substrings by splitting on the specified string (defined in subclasses).nltk.tokenize.simple.CharTokenizer - Tokenize a string into individual characters. If this functionality is ever required directly, use for char in string.nltk.tokenize.simple.SpaceTokenizer - Tokenize a string using the space character as a delimiter, which is the same as s.split(' ').nltk.tokenize.simple.TabTokenizer - Tokenize a string use the tab character as a delimiter, the same as s.split('\t').nltk.tokenize.destructive.NLTKWordTokenizer - The NLTK tokenizer that has improved upon the TreebankWordTokenizer.nltk.tokenize.legality_principle.LegalitySyllableTokenizer - Syllabifies words based on the Legality Principle and Onset Maximization.nltk.tokenize.mwe.MWETokenizer - A tokenizer that processes tokenized text and merges multi-word expressions into single tokens.nltk.tokenize.nist.NISTTokenizer - This NIST tokenizer is sentence-based instead of the original paragraph-based tokenization from mteval-14.pl; The sentence-based tokenization is consistent with the other tokenizers available in NLTK.nltk.tokenize.punkt.PunktSentenceTokenizer - A sentence tokenizer which uses an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences; and then uses that model to find sentence boundaries. This approach has been shown to work well for many European languages.nltk.tokenize.regexp.RegexpTokenizer - A tokenizer that splits a string using a regular expression, which matches either the tokens or the separators between tokens.nltk.tokenize.regexp.BlanklineTokenizer - Tokenize a string, treating any sequence of blank lines as a delimiter. Blank lines are defined as lines containing no characters, except for space or tab characters.nltk.tokenize.regexp.WhitespaceTokenizer - Tokenize a string on whitespace (space, tab, newline). In general, users should use the string split() method instead.nltk.tokenize.regexp.WordPunctTokenizer - Tokenize a text into a sequence of alphabetic and non-alphabetic characters, using the regexp \w+|[^\w\s]+.nltk.tokenize.repp.ReppTokenizer - A class for word tokenization using the REPP parser described in Rebecca Dridan and Stephan Oepen (2012) Tokenization: Returning to a Long Solved Problem - A Survey, Contrastive Experiment, Recommendations, and Toolkit...nltk.tokenize.sexpr.SExprTokenizer - A tokenizer that divides strings into s-expressions. An s-expresion can be either:nltk.tokenize.simple.LineTokenizer - Tokenize a string into its lines, optionally discarding blank lines. This is similar to s.split('\n').nltk.tokenize.sonority_sequencing.SyllableTokenizer - Syllabifies words based on the Sonority Sequencing Principle (SSP).nltk.tokenize.stanford.StanfordTokenizer - Interface to the Stanford Tokenizernltk.tokenize.stanford_segmenter.StanfordSegmenter - Interface to the Stanford Segmenternltk.tokenize.texttiling.TextTilingTokenizer - Tokenize a document into topical sections using the TextTiling algorithm. This algorithm detects subtopic shifts based on the analysis of lexical co-occurrence patterns.nltk.tokenize.toktok.ToktokTokenizer - This is a Python port of the tok-tok.pl from https://github.com/jonsafari/tok-tok/blob/master/tok-tok.plnltk.tokenize.treebank.TreebankWordDetokenizer - The Treebank detokenizer uses the reverse regex operations corresponding to the Treebank tokenizer's regexes.nltk.tokenize.treebank.TreebankWordTokenizer - The Treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank. This is the method that is invoked by word_tokenize(). It assumes that the text has already been segmented into sentences, e...collections.Counternltk.probability.FreqDist - A frequency distribution for the outcomes of an experiment. A frequency distribution records the number of times each outcome of an experiment has occurred. For example, a frequency distribution could be used to record the frequency of each word type in a document...collections.defaultdictnltk.probability.ConditionalFreqDist - A collection of frequency distributions for a single experiment run under different conditions. Conditional frequency distributions are used to record the number of times each sample occurred, given the condition under which the experiment was run...nltk.util.Index - Undocumenteddatetime.tzinfonltk.twitter.api.LocalTimezoneOffsetWithUTC - This is not intended to be a general purpose class for dealing with the local timezone. In particular:dictnltk.collections.OrderedDict - Undocumentednltk.collections.Trie - A Trie implementation for stringsnltk.corpus.reader.framenet.AttrDict - A class that wraps a dict and allows accessing the keys of the dict as if they were attributes. Taken from here: http://stackoverflow.com/a/14620633/8879nltk.corpus.reader.framenet.PrettyDict - Displays an abbreviated repr of values where possible. Inherits from AttrDict, so a callable value will be lazily converted to an actual value.nltk.corpus.reader.panlex_lite.Meaning - Represents a single PanLex meaning. A meaning is a translation set derived from a single source.nltk.featstruct.FeatDict - A feature structure that acts like a Python dictionary. I.e., a mapping from feature identifiers to feature values, where a feature identifier can be a string or a Feature; and where a feature value can be either a basic value (such as a string or an integer), or a nested feature structure...nltk.grammar.FeatStructNonterminal - A feature structure that's also a nonterminal. It acts as its own symbol, and automatically freezes itself when hashed.nltk.probability.ConditionalProbDistI - A collection of probability distributions for a single experiment run under different conditions. Conditional probability distributions are used to estimate the likelihood of each sample, given the condition under which the experiment was run...nltk.probability.ConditionalProbDist - A conditional probability distribution modeling the experiments that were used to generate a conditional frequency distribution. A ConditionalProbDist is constructed from a ConditionalFreqDist and a ProbDist...nltk.probability.DictionaryConditionalProbDist - An alternative ConditionalProbDist that simply wraps a dictionary of ProbDists rather than creating these from FreqDists.nltk.sem.evaluate.Assignment - A dictionary which represents an assignment of values to variables.nltk.sem.evaluate.Valuation - A dictionary which represents a model-theoretic Valuation of non-logical constants. Keys are strings representing the constants to be interpreted, and values correspond to individuals (represented as strings) and n-ary relations (represented as sets of tuples of strings).nltk.sem.glue.GlueDict - No class docstring; 0/2 instance variable, 4/15 methods documentednltk.sem.glue.DrtGlueDict - Undocumentednltk.sem.lfg.FStructure - No class docstring; 1/10 method, 1/3 static method documentedEnvironmentErrornltk.parse.corenlp.CoreNLPServerError - Exceptions associated with the Core NLP server.Exceptionnltk.corpus.reader.framenet.FramenetError - An exception class for framenet-related errors.nltk.corpus.reader.wordnet.WordNetError - An exception class for wordnet-related errors.nltk.featstruct._UnificationFailureError - An exception that is used by _destructively_unify to abort unification when a failure is encountered.nltk.inference.nonmonotonic.ProverParseError - Undocumentednltk.inference.prover9.Prover9Exception - Undocumentednltk.inference.prover9.Prover9FatalException - Undocumentednltk.inference.prover9.Prover9LimitExceededException - Undocumentednltk.inference.resolution.BindingException - Undocumentednltk.inference.resolution.ProverParseError - Undocumentednltk.inference.resolution.UnificationException - Undocumentednltk.inference.tableau.ProverParseError - Undocumentednltk.parse.dependencygraph.DependencyGraphError - Dependency graph exception.nltk.sem.boxer.UnparseableInputException - Undocumentednltk.sem.drt.AnaphoraResolutionException - Undocumentednltk.sem.evaluate.Error - Undocumentednltk.sem.evaluate.Undefined - Undocumentednltk.sem.linearlogic.LinearLogicApplicationException - Undocumentednltk.sem.linearlogic.UnificationException - Undocumentednltk.sem.linearlogic.VariableBindingException - Undocumentednltk.sem.logic.LogicalExpressionException - Undocumentednltk.sem.logic.ExpectedMoreTokensException - Undocumentednltk.sem.logic.UnexpectedTokenException - Undocumentednltk.sem.logic.TypeException - Undocumentednltk.sem.logic.IllegalTypeException - Undocumentednltk.sem.logic.InconsistentTypeHierarchyException - Undocumentednltk.sem.logic.TypeResolutionException - Undocumentednltk.tgrep.TgrepException - Tgrep exception type.frozensetnltk.featstruct.FeatureValueSet - A base feature value that is a set of other base feature values. FeatureValueSet implements SubstituteBindingsI, so it any variable substitutions will be propagated to the elements contained by the set...nltk.featstruct.FeatureValueUnion - A base feature value that represents the union of two or more FeatureValueSet or Variable.nltk.translate.api.Alignment - A storage class for representing alignment between two sequences, s1, s2. In general, an alignment is a set of tuples of the form (i, j, ...) representing an alignment between the i-th element of s1 and the j-th element of s2...gzip.GzipFilenltk.data.BufferedGzipFile - A GzipFile subclass for compatibility with older nltk releases.http.server.BaseHTTPRequestHandlernltk.app.wordnet_app.MyServerHandler - Undocumentedjson.JSONDecodernltk.jsontags.JSONTaggedDecoder - Undocumentedjson.JSONEncodernltk.jsontags.JSONTaggedEncoder - Undocumentedlistnltk.corpus.reader.bnc.BNCSentence - A list of words, augmented by an attribute num used to record the sentence identifier (the n attribute from the XML).nltk.corpus.reader.conll.ConllSRLInstanceList - Set of instances for a single sentencenltk.corpus.reader.framenet.PrettyList - Displays an abbreviated repr of only the first several elements, not the whole list.nltk.corpus.reader.framenet.SpecialList - A list subclass which adds a '_type' attribute for special printing (similar to an AttrDict, though this is NOT an AttrDict subclass).nltk.corpus.reader.semcor.SemcorSentence - A list of words, augmented by an attribute ``num`` used to record the sentence identifier (the ``n`` attribute from the XML).nltk.corpus.reader.switchboard.SwitchboardTurn - A specialized list object used to encode switchboard utterances. The elements of the list are the words in the utterance; and two attributes, speaker and id, are provided to retrieve the spearker identifier and utterance id...nltk.featstruct.FeatList - A list of feature values, where each feature value is either a basic value (such as a string or an integer), or a nested feature structure.nltk.inference.nonmonotonic.SetHolder - A list of sets of Variables.nltk.inference.resolution.Clause - No class docstring; 0/2 instance variable, 6/13 methods documentednltk.sem.drt.PossibleAntecedents - No class docstring; 2/4 methods documentednltk.tree.Tree - A Tree represents a hierarchical grouping of leaves and subtrees. For example, each constituent in a syntax tree is represented by a single Tree.nltk.tree.AbstractParentedTree - An abstract base class for a Tree that automatically maintains pointers to parent nodes. These parent pointers are updated whenever any change is made to a tree's structure. Two subclasses are currently defined:...nltk.tree.MultiParentedTree - A Tree that automatically maintains parent pointers for multi-parented trees. The following are methods for querying the structure of a multi-parented tree: parents(), parent_indices(), left_siblings()...nltk.tree.ImmutableMultiParentedTree - Undocumentednltk.tree.ParentedTree - A Tree that automatically maintains parent pointers for single-parented trees. The following are methods for querying the structure of a parented tree: parent, parent_index, left_sibling, right_sibling...nltk.tree.ImmutableParentedTree - Undocumentednltk.tree.ImmutableTree - No class docstring; 0/2 instance variable, 1/15 method documentednltk.tree.ImmutableMultiParentedTree - Undocumentednltk.tree.ImmutableParentedTree - Undocumentednltk.tree.ImmutableProbabilisticTree - Undocumentednltk.tree.ProbabilisticTree - Undocumentednltk.app.collocations_app.CollocationsModel - Undocumentednltk.app.collocations_app.CollocationsView - Undocumentednltk.app.nemo_app.Zone - Undocumentednltk.app.nemo_app.FindZone - Undocumentednltk.app.nemo_app.ReplaceZone - Undocumentednltk.ccg.api.AbstractCCGCategory - Interface for categories in combinatory grammars.nltk.ccg.api.CCGVar - Class representing a variable CCG category. Used for conjunctions (and possibly type-raising, if implemented as a unary rule).nltk.ccg.api.FunctionalCategory - Class that represents a function application category. Consists of argument and result categories, together with an application direction.nltk.ccg.api.PrimitiveCategory - Class representing primitive categories. Takes a string representation of the category, and a list of strings specifying the morphological subcategories.nltk.ccg.combinator.DirectedBinaryCombinator - Wrapper for the undirected binary combinator. It takes left and right categories, and decides which is to be the function, and which the argument. It then decides whether or not they can be combined.nltk.ccg.combinator.BackwardCombinator - The backward equivalent of the ForwardCombinator class.nltk.ccg.combinator.ForwardCombinator - Class representing combinators where the primary functor is on the left.nltk.ccg.combinator.UndirectedBinaryCombinator - Abstract class for representing a binary combinator. Merely defines functions for checking if the function and argument are able to be combined, and what the resulting category is.nltk.ccg.combinator.UndirectedComposition - Functional composition (harmonic) combinator. Implements rules of the form X/Y Y/Z -> X/Z (B>) And the corresponding backwards and crossed variations.nltk.ccg.combinator.UndirectedFunctionApplication - Class representing function application. Implements rules of the form: X/Y Y -> X (>) And the corresponding backwards application rulenltk.ccg.combinator.UndirectedSubstitution - Substitution (permutation) combinator. Implements rules of the form Y/Z (XY)/Z -> X/Z (<Sx) And other variations.nltk.ccg.combinator.UndirectedTypeRaise - Undirected combinator for type raising.nltk.classify.Sennanltk.tag.senna.SennaChunkTagger - No class docstring; 2/3 methods documentednltk.tag.senna.SennaNERTagger - No class docstring; 1/2 method documentednltk.tag.senna.SennaTagger - No class docstring; 1/2 method documentednltk.classify.weka.ARFF_Formatter - Converts featuresets and labeled featuresets to ARFF-formatted strings, appropriate for input into Weka.nltk.cluster.api.ClusterI - Interface covering basic clustering functionality.nltk.cluster.util.VectorSpaceClusterer - Abstract clusterer which takes tokens and maps them into a vector space. Optionally performs singular value decomposition to reduce the dimensionality.nltk.cluster.em.EMClusterer - The Gaussian EM clusterer models the vectors as being produced by a mixture of k Gaussian sources. The parameters of these sources (prior probability, mean and covariance matrix) are then found to maximise the likelihood of the given data...nltk.cluster.gaac.GAAClusterer - The Group Average Agglomerative starts with each of the N vectors as singleton clusters. It then iteratively merges pairs of clusters which have the closest centroids. This continues until there is only one cluster...nltk.cluster.kmeans.KMeansClusterer - The K-means clusterer starts with k arbitrary chosen means then allocates each vector to the cluster with the closest mean. It then recalculates the means of each cluster as the centroid of the vectors in the cluster...nltk.corpus.reader.bracket_parse.BracketParseCorpusReadernltk.corpus.reader.ycoe.YCOEParseCorpusReader - Specialized version of the standard bracket parse corpus reader that strips out (CODE ...) and (ID ...) nodes.nltk.corpus.reader.mte.MTEFileReader - Class for loading the content of the multext-east corpus. It parses the xml files and does some tag-filtering depending on the given method parameters.nltk.corpus.reader.mte.MTETagConverter - Class for converting msd tags to universal tags, more conversion options are currently not implemented.nltk.corpus.reader.nkjp.XML_Tool - Helper class creating xml file to one without references to nkjp: namespace. That's needed because the XMLCorpusView assumes that one can find short substrings of XML that are valid XML, which is not true if a namespace is declared at top level...nltk.corpus.reader.tagged.TaggedCorpusReadernltk.corpus.reader.ycoe.YCOETaggedCorpusReader - Undocumentednltk.data.LazyLoader - Undocumentednltk.data.PathPointer - An abstract base class for 'path pointers,' used by NLTK's data package to identify specific paths. Two subclasses exist: FileSystemPathPointer identifies a file that can be accessed directly via a given absolute path...nltk.data.FileSystemPathPointer - A path pointer that identifies a file which can be accessed directly via a given absolute path.nltk.data.GzipFileSystemPathPointer - A subclass of FileSystemPathPointer that identifies a gzip-compressed file located at a given absolute path. GzipFileSystemPathPointer is appropriate for loading large gzip-compressed pickle objects efficiently.nltk.data.ZipFilePathPointer - A path pointer that identifies a file contained within a zipfile, which can be accessed by reading that zipfile.nltk.data.SeekableUnicodeStreamReader - A stream reader that automatically encodes the source byte stream into unicode (like codecs.StreamReader); but still supports the seek() and tell() operations correctly. This is in contrast to codecs.StreamReader...nltk.draw.util.CanvasWidget - A collection of graphical elements and bindings used to display a complex object on a Tkinter Canvas. A canvas widget is responsible for managing the Canvas tags and callback bindings necessary to display and interact with the object...nltk.draw.tree.TreeSegmentWidget - A canvas widget that displays a single segment of a hierarchical tree. Each TreeSegmentWidget connects a single "node widget" to a sequence of zero or more "subtree widgets". By default, the bottom of the node is connected to the top of each subtree by a single line...nltk.draw.tree.TreeWidget - A canvas widget that displays a single Tree. TreeWidget manages a group of TreeSegmentWidgets that are used to display a Tree.nltk.draw.util.AbstractContainerWidget - An abstract class for canvas widgets that contain a single child, such as BoxWidget and OvalWidget. Subclasses must define a constructor, which should create any new graphical elements and then call the ...nltk.draw.util.BoxWidget - A canvas widget that places a box around a child widget.nltk.draw.util.BracketWidget - A canvas widget that places a pair of brackets around a child widget.nltk.draw.util.OvalWidget - A canvas widget that places a oval around a child widget.nltk.draw.util.ParenWidget - A canvas widget that places a pair of parenthases around a child widget.nltk.draw.util.ScrollWatcherWidget - A special canvas widget that adjusts its Canvas's scrollregion to always include the bounding boxes of all of its children. The scroll-watcher widget will only increase the size of the Canvas's scrollregion; it will never decrease it.nltk.draw.util.SequenceWidget - A canvas widget that keeps a list of canvas widgets in a horizontal line.nltk.draw.util.SpaceWidget - A canvas widget that takes up space but does not display anything. A SpaceWidget can be used to add space between elements. Each space widget is characterized by a width and a height. If you wish to only create horizontal space, then use a height of zero; and if you wish to only create vertical space, use a width of zero.nltk.draw.util.StackWidget - A canvas widget that keeps a list of canvas widgets in a vertical line.nltk.draw.util.TextWidget - A canvas widget that displays a single string of text.nltk.draw.util.SymbolWidget - A canvas widget that displays special symbols, such as the negation sign and the exists operator. Symbols are specified by name. Currently, the following symbol names are defined: neg, disj, conj, lambda...nltk.inference.api.ModelBuilder - Interface for trying to build a model of set of formulas. Open formulas are assumed to be universally quantified. Both the goal and the assumptions are constrained to be formulas of logic.Expression.nltk.inference.api.ParallelProverBuilder - This class stores both a prover and a model builder and when either prove() or build_model() is called, then both theorem tools are run in parallel. Whichever finishes first, the prover or the model builder, is the result that will be used.nltk.inference.mace.Mace - No class docstring; 1/2 instance variable, 2/3 methods documentednltk.inference.api.Prover - Interface for trying to prove a goal from assumptions. Both the goal and the assumptions are constrained to be formulas of logic.Expression.nltk.inference.api.ParallelProverBuilder - This class stores both a prover and a model builder and when either prove() or build_model() is called, then both theorem tools are run in parallel. Whichever finishes first, the prover or the model builder, is the result that will be used.nltk.inference.prover9.Prover9 - No class docstring; 1/3 instance variable, 4/5 methods documentednltk.inference.resolution.ResolutionProver - No class docstring; 0/1 class variable, 0/1 constant, 1/2 method documentednltk.inference.tableau.TableauProver - Undocumentednltk.inference.api.TheoremToolCommand - This class holds a goal and a list of assumptions to be used in proving or model building.nltk.inference.api.BaseTheoremToolCommand - This class holds a goal and a list of assumptions to be used in proving or model building.nltk.inference.api.BaseModelBuilderCommand - This class holds a ModelBuilder, a goal, and a list of assumptions. When build_model() is called, the ModelBuilder is executed with the goal and assumptions.nltk.inference.api.ParallelProverBuilderCommand - This command stores both a prover and a model builder and when either prove() or build_model() is called, then both theorem tools are run in parallel. Whichever finishes first, the prover or the model builder, is the result that will be used.nltk.inference.mace.MaceCommand - A MaceCommand specific to the Mace model builder. It contains a print_assumptions() method that is used to print the list of assumptions in multiple formats.nltk.inference.api.BaseProverCommand - This class holds a Prover, a goal, and a list of assumptions. When prove() is called, the Prover is executed with the goal and assumptions.nltk.inference.api.ParallelProverBuilderCommand - This command stores both a prover and a model builder and when either prove() or build_model() is called, then both theorem tools are run in parallel. Whichever finishes first, the prover or the model builder, is the result that will be used.nltk.inference.prover9.Prover9Command - A ProverCommand specific to the Prover9 prover. It contains the a print_assumptions() method that is used to print the list of assumptions in multiple formats.nltk.inference.resolution.ResolutionProverCommand - No class docstring; 0/3 instance variable, 2/3 methods, 1/1 static method documentednltk.inference.tableau.TableauProverCommand - No class docstring; 1/1 method documentednltk.inference.api.ModelBuilderCommand - This class holds a ModelBuilder, a goal, and a list of assumptions. When build_model() is called, the ModelBuilder is executed with the goal and assumptions.nltk.inference.api.BaseModelBuilderCommand - This class holds a ModelBuilder, a goal, and a list of assumptions. When build_model() is called, the ModelBuilder is executed with the goal and assumptions.nltk.inference.api.ParallelProverBuilderCommand - This command stores both a prover and a model builder and when either prove() or build_model() is called, then both theorem tools are run in parallel. Whichever finishes first, the prover or the model builder, is the result that will be used.nltk.inference.mace.MaceCommand - A MaceCommand specific to the Mace model builder. It contains a print_assumptions() method that is used to print the list of assumptions in multiple formats.nltk.inference.api.ModelBuilderCommandDecorator - A base decorator for the ModelBuilderCommand class from which other prover command decorators can extend.nltk.inference.api.ProverCommand - This class holds a Prover, a goal, and a list of assumptions. When prove() is called, the Prover is executed with the goal and assumptions.nltk.inference.api.BaseProverCommand - This class holds a Prover, a goal, and a list of assumptions. When prove() is called, the Prover is executed with the goal and assumptions.nltk.inference.api.ParallelProverBuilderCommand - This command stores both a prover and a model builder and when either prove() or build_model() is called, then both theorem tools are run in parallel. Whichever finishes first, the prover or the model builder, is the result that will be used.nltk.inference.prover9.Prover9Command - A ProverCommand specific to the Prover9 prover. It contains the a print_assumptions() method that is used to print the list of assumptions in multiple formats.nltk.inference.resolution.ResolutionProverCommand - No class docstring; 0/3 instance variable, 2/3 methods, 1/1 static method documentednltk.inference.tableau.TableauProverCommand - No class docstring; 1/1 method documentednltk.inference.api.ProverCommandDecorator - A base decorator for the ProverCommand class from which other prover command decorators can extend.nltk.inference.nonmonotonic.ClosedDomainProver - This is a prover decorator that adds domain closure assumptions before proving.nltk.inference.nonmonotonic.ClosedWorldProver - This is a prover decorator that completes predicates before proving.nltk.inference.nonmonotonic.UniqueNamesProver - This is a prover decorator that adds unique names assumptions before proving.nltk.inference.api.TheoremToolCommandDecorator - A base decorator for the ProverCommandDecorator and ModelBuilderCommandDecorator classes from which decorators can extend.nltk.inference.api.ModelBuilderCommandDecorator - A base decorator for the ModelBuilderCommand class from which other prover command decorators can extend.nltk.inference.api.ProverCommandDecorator - A base decorator for the ProverCommand class from which other prover command decorators can extend.nltk.inference.nonmonotonic.ClosedDomainProver - This is a prover decorator that adds domain closure assumptions before proving.nltk.inference.nonmonotonic.ClosedWorldProver - This is a prover decorator that completes predicates before proving.nltk.inference.nonmonotonic.UniqueNamesProver - This is a prover decorator that adds unique names assumptions before proving.nltk.inference.discourse.ReadingCommand - No class docstring; 4/4 methods documentednltk.inference.discourse.CfgReadingCommand - No class docstring; 0/2 instance variable, 4/4 methods documentednltk.inference.discourse.DrtGlueReadingCommand - No class docstring; 0/1 instance variable, 5/5 methods documentednltk.internals.Counter - A counter that auto-increments each time its value is read.nltk.lazyimport.LazyModule - Lazy module class.nltk.lm.api.LanguageModel - ABC for Language Models.nltk.lm.Lidstone - Provides Lidstone-smoothed scores.nltk.lm.Laplace - Implements Laplace (add one) smoothing.nltk.lm.MLE - Class for providing MLE ngram model scores.nltk.lm.models.InterpolatedLanguageModel - Logic common to all interpolated language models.nltk.lm.KneserNeyInterpolated - Interpolated version of Kneser-Ney smoothing.nltk.lm.WittenBellInterpolated - Interpolated version of Witten-Bell smoothing.nltk.lm.api.Smoothing - Ngram Smoothing Interfacenltk.lm.smoothing.KneserNey - Kneser-Ney Smoothing.nltk.lm.smoothing.WittenBell - Witten-Bell smoothing.nltk.lm.NgramCounter - Class for counting ngrams.nltk.lm.Vocabulary - Stores language model vocabulary.nltk.metrics.association.NgramAssocMeasures - An abstract class defining a collection of generic association measures. Each public method returns a score, taking the following arguments:nltk.metrics.association.BigramAssocMeasures - A collection of bigram association measures. Each association measure is provided as a function with three arguments:nltk.metrics.association.QuadgramAssocMeasures - A collection of quadgram association measures. Each association measure is provided as a function with five arguments:nltk.metrics.association.TrigramAssocMeasures - A collection of trigram association measures. Each association measure is provided as a function with four arguments:nltk.parse.ParserInltk.ccg.chart.CCGChartParser - Chart parser for CCGs. Based largely on the ChartParser class from NLTK.nltk.chunk.api.ChunkParserI - A processing interface for identifying non-overlapping groups in unrestricted text. Typically, chunk parsers are used to find base syntactic constituents, such as base noun phrases. Unlike ParserI, ChunkParserI...nltk.chunk.named_entity.NEChunkParser - Expected input: list of pos-tagged wordsnltk.chunk.regexp.RegexpChunkParser - A regular expression based chunk parser. RegexpChunkParser uses a sequence of "rules" to find chunks of a single type within a text. The chunking of the text is encoded using a ChunkString, and each rule acts by modifying the chunking in the ...nltk.chunk.regexp.RegexpParser - A grammar based chunk parser. chunk.RegexpParser uses a set of regular expression patterns to specify the behavior of the parser. The chunking of the text is encoded using a ChunkString, and each rule acts by modifying the chunking in the ...nltk.parse.transitionparser.TransitionParser - Class for transition based parser. Implement 2 algorithms which are "arc-standard" and "arc-eager"nltk.probability.ProbDistI - A probability distribution for the outcomes of an experiment. A probability distribution specifies how likely it is that an experiment will have any given outcome. For example, a probability distribution could be used to predict the probability that a token in a document will have a given type...nltk.probability.CrossValidationProbDist - The cross-validation estimate for the probability distribution of the experiment used to generate a set of frequency distribution. The "cross-validation estimate" for the probability of a sample is found by averaging the held-out estimates for the sample in each pair of frequency distributions.nltk.probability.DictionaryProbDist - A probability distribution whose probabilities are directly specified by a given dictionary. The given dictionary maps samples to probabilities.nltk.probability.HeldoutProbDist - The heldout estimate for the probability distribution of the experiment used to generate two frequency distributions. These two frequency distributions are called the "heldout frequency distribution" and the "base frequency distribution...nltk.probability.KneserNeyProbDist - Kneser-Ney estimate of a probability distribution. This is a version of back-off that counts how likely an n-gram is provided the n-1-gram had been seen in training. Extends the ProbDistI interface, requires a trigram FreqDist instance to train on...nltk.probability.LidstoneProbDist - The Lidstone estimate for the probability distribution of the experiment used to generate a frequency distribution. The "Lidstone estimate" is parameterized by a real number gamma, which typically ranges from 0 to 1...nltk.probability.ELEProbDist - The expected likelihood estimate for the probability distribution of the experiment used to generate a frequency distribution. The "expected likelihood estimate" approximates the probability of a sample with count ...nltk.probability.LaplaceProbDist - The Laplace estimate for the probability distribution of the experiment used to generate a frequency distribution. The "Laplace estimate" approximates the probability of a sample with count c from an experiment with ...nltk.probability.MLEProbDist - The maximum likelihood estimate for the probability distribution of the experiment used to generate a frequency distribution. The "maximum likelihood estimate" approximates the probability of each sample as the frequency of that sample in the frequency distribution.nltk.probability.MutableProbDist - An mutable probdist where the probabilities may be easily modified. This simply copies an existing probdist, storing the probability values in a mutable dictionary and providing an update method.nltk.probability.RandomProbDist - Generates a random probability distribution whereby each sample will be between 0 and 1 with equal probability (uniform random distribution. Also called a continuous uniform distribution).nltk.probability.SimpleGoodTuringProbDist - SimpleGoodTuring ProbDist approximates from frequency to frequency of frequency into a linear line under log space by linear regression. Details of Simple Good-Turing algorithm can be found in:nltk.probability.UniformProbDist - A probability distribution that assigns equal probability to each sample in a given set; and a zero probability to all other samples.nltk.probability.WittenBellProbDist - The Witten-Bell estimate of a probability distribution. This distribution allocates uniform probability mass to as yet unseen events by using the number of events that have only been seen once. The probability mass reserved for unseen events is equal to ...nltk.sentiment.vader.SentimentIntensityAnalyzer - Give a sentiment intensity score to sentences.nltk.sentiment.vader.SentiText - Identify sentiment-relevant string-level properties of input text.nltk.sentiment.vader.VaderConstants - A class to keep the Vader lists and constants.nltk.stem.api.StemmerI - A processing interface for removing morphological affixes from words. This process is known as stemming.nltk.stem.arlstem.ARLSTem - ARLSTem stemmer : a light Arabic Stemming algorithm without any dictionary. Department of Telecommunication & Information Processing. USTHB University, Algiers, Algeria. ARLSTem.stem(token) returns the Arabic stem for the input token...nltk.stem.arlstem2.ARLSTem2 - Return a stemmed Arabic word after removing affixes. This an improved version of the previous algorithm, which reduces under-stemming errors. Typically used in Arabic search engine, information retrieval and NLP.nltk.stem.cistem.Cistem - CISTEM Stemmer for Germannltk.stem.isri.ISRIStemmer - ISRI Arabic stemmer based on algorithm: Arabic Stemming without a root dictionary. Information Science Research Institute. University of Nevada, Las Vegas, USA.nltk.stem.lancaster.LancasterStemmer - Lancaster Stemmernltk.stem.porter.PorterStemmer - A word stemmer based on the Porter stemming algorithm.nltk.stem.snowball.PorterStemmer - A word stemmer based on the original Porter stemming algorithm.nltk.stem.regexp.RegexpStemmer - A stemmer that uses regular expressions to identify morphological affixes. Any substrings that match the regular expressions will be removed.nltk.stem.rslp.RSLPStemmer - A stemmer for Portuguese.nltk.stem.snowball._LanguageSpecificStemmer - This helper subclass offers the possibility to invoke a specific stemmer directly. This is useful if you already know the language to be stemmed at runtime.nltk.stem.snowball._ScandinavianStemmer - This subclass encapsulates a method for defining the string region R1. It is used by the Danish, Norwegian, and Swedish stemmer.nltk.stem.snowball.DanishStemmer - The Danish Snowball stemmer.nltk.stem.snowball.NorwegianStemmer - The Norwegian Snowball stemmer.nltk.stem.snowball.SwedishStemmer - The Swedish Snowball stemmer.nltk.stem.snowball._StandardStemmer - This subclass encapsulates two methods for defining the standard versions of the string regions R1, R2, and RV.nltk.stem.snowball.ArabicStemmer - https://github.com/snowballstem/snowball/blob/master/algorithms/arabic/stem_Unicode.sbl (Original Algorithm) The Snowball Arabic light Stemmer Algorithm : Assem Chellinltk.stem.snowball.DutchStemmer - The Dutch Snowball stemmer.nltk.stem.snowball.EnglishStemmer - The English Snowball stemmer.nltk.stem.snowball.FinnishStemmer - The Finnish Snowball stemmer.nltk.stem.snowball.FrenchStemmer - The French Snowball stemmer.nltk.stem.snowball.GermanStemmer - The German Snowball stemmer.nltk.stem.snowball.ItalianStemmer - The Italian Snowball stemmer.nltk.stem.snowball.PortugueseStemmer - The Portuguese Snowball stemmer.nltk.stem.snowball.RomanianStemmer - The Romanian Snowball stemmer.nltk.stem.snowball.SpanishStemmer - The Spanish Snowball stemmer.nltk.stem.snowball.HungarianStemmer - The Hungarian Snowball stemmer.nltk.stem.snowball.PorterStemmer - A word stemmer based on the original Porter stemming algorithm.nltk.stem.snowball.RussianStemmer - The Russian Snowball stemmer.nltk.stem.snowball.SnowballStemmer - Snowball Stemmernltk.tag.api.TaggerI - A processing interface for assigning a tag to each token in a list. Tags are case sensitive strings that identify some property of each token, such as its part of speech or its sense.nltk.classify.senna.Senna - No class docstring; 0/3 instance variable, 0/1 constant, 4/5 methods documentednltk.parse.corenlp.GenericCoreNLPParser - Interface to the CoreNLP Parser.nltk.parse.corenlp.CoreNLPDependencyParser - Dependency parser.nltk.parse.corenlp.CoreNLPParser - No summarynltk.tag.api.FeaturesetTaggerI - A tagger that requires tokens to be featuresets. A featureset is a dictionary that maps from feature names to feature values. See nltk.classify for more information about features and featuresets.nltk.tag.sequential.ClassifierBasedTagger - A sequential tagger that uses a classifier to choose the tag for each token in a sentence. The featureset input for the classifier is generated by a feature detector function:nltk.tag.sequential.ClassifierBasedPOSTagger - A classifier based part of speech tagger.nltk.tag.crf.CRFTagger - A module for POS tagging using CRFSuite https://pypi.python.org/pypi/python-crfsuitenltk.tag.hmm.HiddenMarkovModelTagger - Hidden Markov model class, a generative model for labelling sequence data. These models define the joint probability of a sequence of symbols and their labels (state transitions) as the product of the starting state probability, the probability of each state transition, and the probability of each observation being generated from each state...nltk.tag.hunpos.HunposTagger - a model trained on training datanltk.tag.perceptron.PerceptronTagger - Greedy Averaged Perceptron tagger, as implemented by Matthew Honnibal. See more implementation details here:nltk.tag.sequential.SequentialBackoffTagger - An abstract base class for taggers that tags words sequentially, left to right. Tagging of individual words is performed by the choose_tag() method, which should be defined by subclasses. If a tagger is unable to determine a tag for the specified token, then its backoff tagger is consulted.nltk.tag.sequential.ClassifierBasedTagger - A sequential tagger that uses a classifier to choose the tag for each token in a sentence. The featureset input for the classifier is generated by a feature detector function:nltk.tag.sequential.ClassifierBasedPOSTagger - A classifier based part of speech tagger.nltk.tag.sequential.ContextTagger - An abstract base class for sequential backoff taggers that choose a tag for a token based on the value of its "context". Different subclasses are used to define different contexts.nltk.tag.sequential.AffixTagger - A tagger that chooses a token's tag based on a leading or trailing substring of its word string. (It is important to note that these substrings are not necessarily "true" morphological affixes). In particular, a fixed-length substring of the word is looked up in a table, and the corresponding tag is returned...nltk.tag.sequential.NgramTagger - A tagger that chooses a token's tag based on its word string and on the preceding n word's tags. In particular, a tuple (tags[i-n:i-1], words[i]) is looked up in a table, and the corresponding tag is returned...nltk.tag.sequential.BigramTagger - A tagger that chooses a token's tag based its word string and on the preceding words' tag. In particular, a tuple consisting of the previous tag and the word is looked up in a table, and the corresponding tag is returned.nltk.tag.sequential.TrigramTagger - A tagger that chooses a token's tag based its word string and on the preceding two words' tags. In particular, a tuple consisting of the previous two tags and the word is looked up in a table, and the corresponding tag is returned.nltk.tag.sequential.UnigramTagger - Unigram Taggernltk.tag.sequential.DefaultTagger - A tagger that assigns the same tag to every token.nltk.tag.sequential.RegexpTagger - Regular Expression Taggernltk.tag.stanford.StanfordTagger - An interface to Stanford taggers. Subclasses must define:nltk.tag.stanford.StanfordNERTagger - A class for Named-Entity Tagging with Stanford Tagger. The input is the paths to:nltk.tag.stanford.StanfordPOSTagger - a model trained on training datanltk.tag.tnt.TnT - TnT - Statistical POS taggernltk.tag.ClassifierBasedTaggernltk.chunk.named_entity.NEChunkParserTagger - The IOB tagger used by the chunk parser.nltk.tag.perceptron.AveragedPerceptron - An averaged perceptron, as implemented by Matthew Honnibal.nltk.tag.TaggerInltk.tag.brill.BrillTagger - Brill's transformational rule-based tagger. Brill taggers use an initial tagger (such as tag.DefaultTagger) to assign an initial tag sequence to a text; and then apply an ordered list of transformational rules to correct the tags of individual tokens...nltk.tbl.Featurenltk.tag.brill.Pos - Feature which examines the tags of nearby tokens.nltk.tag.brill.Word - Feature which examines the text (word) of nearby tokens.nltk.tbl.feature.Feature - An abstract base class for Features. A Feature is a combination of a specific property-computing method and a list of relative positions to apply that method to.nltk.tbl.rule.TagRule - An interface for tag transformations on a tagged corpus, as performed by tbl taggers. Each transformation finds all tokens in the corpus that are tagged with a specific original tag and satisfy a specific condition, and replaces their tags with a replacement tag...nltk.tbl.rule.Rule - A Rule checks the current corpus position for a certain set of conditions; if they are all fulfilled, the Rule is triggered, meaning that it will change tag A to tag B. For other tags than A, nothing happens.nltk.tbl.template.BrillTemplateI - An interface for generating lists of transformational rules that apply at given sentence positions. BrillTemplateI is used by Brill training algorithms to generate candidate rules.nltk.tbl.template.Template - A tbl Template that generates a list of L{Rule}s that apply at a given sentence position. In particular, each C{Template} is parameterized by a list of independent features (a combination of a specific property to extract and a list C{L} of relative positions at which to extract it) and generates all Rules that:...nltk.test.unit.lm.test_counter.TestNgramCounter - Tests for NgramCounter that only involve lookup, no modification.nltk.test.unit.lm.test_counter.TestNgramCounterTraining - Undocumentednltk.test.unit.lm.test_models.TestLaplaceBigram - Unit tests for Laplace classnltk.test.unit.lm.test_models.TestLidstoneBigram - Unit tests for Lidstone classnltk.test.unit.lm.test_models.TestLidstoneTrigram - Undocumentednltk.test.unit.lm.test_models.TestMleBigram - Unit tests for MLE ngram model.nltk.test.unit.lm.test_models.TestMleTrigram - MLE trigram model testsnltk.test.unit.lm.test_models.TestNgramModelTextGeneration - Using MLE model, generate some text.nltk.test.unit.test_data.TestData - Undocumentednltk.test.unit.test_rte_classify.TestRTEClassifier - Undocumentednltk.test.unit.test_tokenize.TestTokenize - No class docstring; 11/16 methods documentednltk.test.unit.test_twitter_auth.TestCredentials - Tests that Twitter credentials from a file are handled correctly.nltk.tokenize.casual.TweetTokenizer - Tokenizer for tweets.nltk.tokenize.destructive.MacIntyreContractions - List of contractions adapted from Robert MacIntyre's tokenizer.nltk.translate.bleu_score.SmoothingFunction - This is an implementation of the smoothing techniques for segment-level BLEU scores that was presented in Boxing Chen and Collin Cherry (2014) A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU...nltk.translate.IBMModelnltk.translate.ibm1.IBMModel1 - Lexical translation model that ignores word ordernltk.translate.ibm2.IBMModel2 - Lexical translation model that considers word ordernltk.translate.ibm3.IBMModel3 - Translation model that considers how a word can be aligned to multiple words in another languagenltk.translate.ibm4.IBMModel4 - Translation model that reorders output words based on their type and their distance from other related words in the output sentencenltk.translate.ibm5.IBMModel5 - Translation model that keeps track of vacant positions in the target sentence to decide where to place translated wordsnltk.twitter.api.BasicTweetHandler - Minimal implementation of TweetHandler.nltk.twitter.api.TweetHandlerI - Interface class whose subclasses should implement a handle method that Twitter clients can delegate to.nltk.twitter.twitterclient.TweetViewer - Handle data by sending it to the terminal.nltk.twitter.twitterclient.TweetWriter - Handle data by writing it to a file.nltk.util.AbstractLazySequencenltk.corpus.reader.util.ConcatenatedCorpusView - A 'view' of a corpus file that joins together one or more StreamBackedCorpusViews<StreamBackedCorpusView>. At most one file handle is left open at any time.nltk.corpus.reader.util.StreamBackedCorpusView - A 'view' of a corpus file, which acts like a sequence of tokens: it can be accessed by index, iterated over, etc. However, the tokens are only constructed as-needed -- the entire corpus is never stored in memory at once.nltk.corpus.reader.aligned.AlignedSentCorpusView - A specialized corpus view for aligned sentences. AlignedSentCorpusView objects are typically created by AlignedCorpusReader (not directly by nltk users).nltk.corpus.reader.chasen.ChasenCorpusView - A specialized corpus view for ChasenReader. Similar to TaggedCorpusView, but this'll use fixed sets of word and sentence tokenizer.nltk.corpus.reader.chunked.ChunkedCorpusView - Undocumentednltk.corpus.reader.dependency.DependencyCorpusView - Undocumentednltk.corpus.reader.indian.IndianCorpusView - Undocumentednltk.corpus.reader.ipipan.IPIPANCorpusView - Undocumentednltk.corpus.reader.opinion_lexicon.IgnoreReadmeCorpusView - This CorpusView is used to skip the initial readme block of the corpus.nltk.corpus.reader.senseval.SensevalCorpusView - Undocumentednltk.corpus.reader.tagged.TaggedCorpusView - A specialized corpus view for tagged documents. It can be customized via flags to divide the tagged corpus documents up by sentence or paragraph, and to include or omit part of speech tags. TaggedCorpusView...nltk.corpus.reader.TEICorpusView - Undocumentednltk.corpus.reader.util.PickleCorpusView - A stream backed corpus view for corpus files that consist of sequences of serialized Python objects (serialized using pickle.dump). One use case for this class is to store the result of running feature detection on a corpus to disk...nltk.corpus.reader.xmldocs.XMLCorpusView - A corpus view that selects out specified elements from an XML file, and provides a flat list-like interface for accessing them. (Note: XMLCorpusView is not used by XMLCorpusReader itself, but may be used by subclasses of ...nltk.corpus.reader.bnc.BNCWordView - A stream backed corpus view specialized for use with the BNC corpus.nltk.corpus.reader.mte.MTECorpusView - Class for lazy viewing the MTE Corpus.nltk.corpus.reader.nkjp.NKJPCorpus_Header_View - No class docstring; 0/1 instance variable, 1/3 method documentednltk.corpus.reader.nkjp.NKJPCorpus_Morph_View - A stream backed corpus view specialized for use with ann_morphosyntax.xml files in NKJP corpus.nltk.corpus.reader.nkjp.NKJPCorpus_Segmentation_View - A stream backed corpus view specialized for use with ann_segmentation.xml files in NKJP corpus.nltk.corpus.reader.nkjp.NKJPCorpus_Text_View - A stream backed corpus view specialized for use with text.xml files in NKJP corpus.nltk.corpus.reader.semcor.SemcorWordView - A stream backed corpus view specialized for use with the BNC corpus.nltk.util.LazyConcatenationnltk.corpus.reader.framenet.PrettyLazyConcatenation - Displays an abbreviated repr of only the first several elements, not the whole list.nltk.util.LazyIteratorListnltk.corpus.reader.framenet.PrettyLazyIteratorList - Displays an abbreviated repr of only the first several elements, not the whole list.nltk.util.LazyMapnltk.corpus.reader.framenet.PrettyLazyMap - Displays an abbreviated repr of only the first several elements, not the whole list.objectnltk.app.chartparser_app.ChartComparer - No summarynltk.app.chartparser_app.ChartMatrixView - A view of a chart that displays the contents of the corresponding matrix.nltk.app.chartparser_app.ChartParserApp - No class docstring; 0/19 instance variable, 0/10 constant, 4/46 methods documentednltk.app.chartparser_app.ChartResultsView - Undocumentednltk.app.chartparser_app.ChartView - A component for viewing charts. This is used by ChartParserApp to allow students to interactively experiment with various chart parsing techniques. It is also used by Chart.draw().nltk.app.chartparser_app.EdgeRule - To create an edge rule, make an empty base class that uses EdgeRule as the first base class, and the basic rule as the second base class. (Order matters!)nltk.app.chartparser_app.BottomUpEdgeRule - Undocumentednltk.app.chartparser_app.BottomUpLeftCornerEdgeRule - Undocumentednltk.app.chartparser_app.FundamentalEdgeRule - Undocumentednltk.app.chartparser_app.TopDownPredictEdgeRule - Undocumentednltk.app.chunkparser_app.RegexpChunkApp - A graphical tool for exploring the regular expression based chunk parser nltk.chunk.RegexpChunkParser.nltk.app.concordance_app.ConcordanceSearchModel - Undocumentednltk.app.concordance_app.ConcordanceSearchView - Undocumentednltk.app.rdparser_app.RecursiveDescentApp - A graphical tool for exploring the recursive descent parser. The tool displays the parser's tree and the remaining text, and allows the user to control the parser's operation. In particular, the user can expand subtrees on the frontier, match tokens on the frontier against the text, and backtrack...nltk.app.srparser_app.ShiftReduceApp - A graphical tool for exploring the shift-reduce parser. The tool displays the parser's stack and the remaining text, and allows the user to control the parser's operation. In particular, the user can shift tokens onto the stack, and can perform reductions on the top elements of the stack...nltk.app.wordnet_app.Reference - A reference to a page that may be generated by page_wordnltk.ccg.api.Direction - Class representing the direction of a function application. Also contains maintains information as to which combinators may be used with the category.nltk.ccg.lexicon.CCGLexicon - Class representing a lexicon for CCG grammars.nltk.ccg.lexicon.Token - Class representing a token.nltk.chat.util.Chat - No class docstring; 0/3 instance variable, 3/6 methods documentednltk.chunk.regexp.ChunkString - A string-based encoding of a particular chunking of a text. Internally, the ChunkString class uses a single string to encode the chunking of the input text. This string contains a sequence of angle-bracket delimited tags, with chunking indicated by braces...nltk.chunk.regexp.RegexpChunkRule - A rule specifying how to modify the chunking in a ChunkString, using a transformational regular expression. The RegexpChunkRule class itself can be used to implement any transformational rule based on regular expressions...nltk.chunk.regexp.ChunkRule - A rule specifying how to add chunks to a ChunkString, using a matching tag pattern. When applied to a ChunkString, it will find any substring that matches this tag pattern and that is not already part of a chunk, and create a new chunk containing that substring.nltk.chunk.regexp.ChunkRuleWithContext - A rule specifying how to add chunks to a ChunkString, using three matching tag patterns: one for the left context, one for the chunk, and one for the right context. When applied to a ChunkString, it will find any substring that matches the chunk tag pattern, is surrounded by substrings that match the two context patterns, and is not already part of a chunk; and create a new chunk containing the substring that matched the chunk tag pattern.nltk.chunk.regexp.ExpandLeftRule - A rule specifying how to expand chunks in a ChunkString to the left, using two matching tag patterns: a left pattern, and a right pattern. When applied to a ChunkString, it will find any chunk whose beginning matches right pattern, and immediately preceded by a strip whose end matches left pattern...nltk.chunk.regexp.ExpandRightRule - A rule specifying how to expand chunks in a ChunkString to the right, using two matching tag patterns: a left pattern, and a right pattern. When applied to a ChunkString, it will find any chunk whose end matches left pattern, and immediately followed by a strip whose beginning matches right pattern...nltk.chunk.regexp.MergeRule - A rule specifying how to merge chunks in a ChunkString, using two matching tag patterns: a left pattern, and a right pattern. When applied to a ChunkString, it will find any chunk whose end matches left pattern, and immediately followed by a chunk whose beginning matches right pattern...nltk.chunk.regexp.SplitRule - A rule specifying how to split chunks in a ChunkString, using two matching tag patterns: a left pattern, and a right pattern. When applied to a ChunkString, it will find any chunk that matches the left pattern followed by the right pattern...nltk.chunk.regexp.StripRule - A rule specifying how to remove strips to a ChunkString, using a matching tag pattern. When applied to a ChunkString, it will find any substring that matches this tag pattern and that is contained in a chunk, and remove it from that chunk, thus creating two new chunks.nltk.chunk.regexp.UnChunkRule - A rule specifying how to remove chunks to a ChunkString, using a matching tag pattern. When applied to a ChunkString, it will find any complete chunk that matches this tag pattern, and un-chunk it.nltk.chunk.util.ChunkScore - A utility class for scoring chunk parsers. ChunkScore can evaluate a chunk parser's output, based on a number of statistics (precision, recall, f-measure, misssed chunks, incorrect chunks). It can also combine the scores from the parsing of multiple texts; this makes it significantly easier to evaluate a chunk parser that operates one sentence at a time.nltk.classify.api.ClassifierI - A processing interface for labeling tokens with a single category label (or "class"). Labels are typically strs or ints, but can be any immutable type. The set of labels that the classifier chooses from must be fixed and finite.nltk.classify.decisiontree.DecisionTreeClassifier - No class docstring; 0/4 instance variable, 3/8 methods, 1/6 static method documentednltk.classify.maxent.MaxentClassifier - A maximum entropy classifier (also known as a "conditional exponential classifier"). This classifier is parameterized by a set of "weights", which are used to combine the joint-features that are generated from a featureset by an "encoding"...nltk.classify.maxent.TadmMaxentClassifier - Undocumentednltk.classify.naivebayes.NaiveBayesClassifier - A Naive Bayes classifier. Naive Bayes classifiers are paramaterized by two probability distributions:nltk.classify.positivenaivebayes.PositiveNaiveBayesClassifier - No class docstring; 1/1 static method documentednltk.classify.scikitlearn.SklearnClassifier - Wrapper for scikit-learn classifiers.nltk.classify.weka.WekaClassifier - Undocumentednltk.classify.api.MultiClassifierI - A processing interface for labeling tokens with zero or more category labels (or "labels"). Labels are typically strs or ints, but can be any immutable type. The set of labels that the multi-classifier chooses from must be fixed and finite.nltk.classify.maxent.MaxentFeatureEncodingI - A mapping that converts a set of input-feature values to a vector of joint-feature values, given a label. This conversion is necessary to translate featuresets into a format that can be used by maximum entropy models.nltk.classify.maxent.BinaryMaxentFeatureEncoding - A feature encoding that generates vectors containing a binary joint-features of the form:nltk.classify.maxent.GISEncoding - A binary feature encoding which adds one new joint-feature to the joint-features defined by ``BinaryMaxentFeatureEncoding``: a correction feature, whose value is chosen to ensure that the sparse vector always sums to a constant non-negative number...nltk.classify.maxent.TadmEventMaxentFeatureEncoding - Undocumentednltk.classify.maxent.FunctionBackedMaxentFeatureEncoding - A feature encoding that calls a user-supplied function to map a given featureset/label pair to a sparse joint-feature vector.nltk.classify.maxent.TypedMaxentFeatureEncoding - A feature encoding that generates vectors containing integer, float and binary joint-features of the form:nltk.classify.rte_classify.RTEFeatureExtractor - This builds a bag of words for both the text and the hypothesis after throwing away some stopwords, then calculates overlap and difference.nltk.classify.svm.SvmClassifier - Undocumentednltk.classify.textcat.TextCat - No class docstring; 0/2 instance variable, 0/1 class variable, 0/2 constant, 5/6 methods documentednltk.classify.util.CutoffChecker - A helper class that implements cutoff checks based on number of iterations and log likelihood.nltk.cluster.util._DendrogramNode - Tree node of a dendrogram.nltk.cluster.util.Dendrogram - Represents a dendrogram, a tree with a specified branching order. This must be initialised with the leaf items, then iteratively call merge for each branch. This class constructs a tree representing the order of calls to the merge function.nltk.collections.AbstractLazySequence - An abstract base class for read-only sequences whose values are computed as needed. Lazy sequences act like tuples -- they can be indexed, sliced, and iterated over; but they may not be modified.nltk.collections.LazyConcatenation - A lazy sequence formed by concatenating a list of lists. This underlying list of lists may itself be lazy. LazyConcatenation maintains an index that it uses to keep track of the relationship between offsets in the concatenated lists and offsets in the sublists.nltk.collections.LazyIteratorList - Wraps an iterator, loading its elements on demand and making them subscriptable. __repr__ displays only the first few elements.nltk.collections.LazyMap - A lazy sequence whose elements are formed by applying a given function to each element in one or more underlying lists. The function is applied lazily -- i.e., when you read a value from the list, LazyMap...nltk.collections.LazyZip - A lazy sequence whose elements are tuples, each containing the i-th element from each of the argument sequences. The returned list is truncated in length to the length of the shortest argument sequence...nltk.collections.LazyEnumerate - A lazy sequence whose elements are tuples, each ontaining a count (from zero) and a value yielded by underlying sequence. LazyEnumerate is useful for obtaining an indexed list. The tuples are constructed lazily -- i...nltk.collections.LazySubsequence - A subsequence produced by slicing a lazy sequence. This slice keeps a reference to its source sequence, and generates its values by looking them up in the source sequence.nltk.collocations.AbstractCollocationFinder - An abstract base class for collocation finders whose purpose is to collect collocation candidate frequencies, filter and rank them.nltk.collocations.BigramCollocationFinder - A tool for the finding and ranking of bigram collocations or other association measures. It is often useful to use from_words() rather than constructing an instance directly.nltk.collocations.QuadgramCollocationFinder - A tool for the finding and ranking of quadgram collocations or other association measures. It is often useful to use from_words() rather than constructing an instance directly.nltk.collocations.TrigramCollocationFinder - A tool for the finding and ranking of trigram collocations or other association measures. It is often useful to use from_words() rather than constructing an instance directly.nltk.corpus.reader.CategorizedCorpusReader - A mixin class used to aid in the implementation of corpus readers for categorized corpora. This class defines the method categories(), which returns a list of the categories for the corpus or for a specified set of fileids; and overrides ...nltk.corpus.reader.CategorizedBracketParseCorpusReader - A reader for parsed corpora whose documents are divided into categories based on their file identifiers. @author: Nathan Schneider <nschneid@cs.cmu.edu>nltk.corpus.reader.CategorizedPlaintextCorpusReader - A reader for plaintext corpora whose documents are divided into categories based on their file identifiers.nltk.corpus.reader.CategorizedSentencesCorpusReader - A reader for corpora in which each row represents a single instance, mainly a sentence. Istances are divided into categories based on their file identifiers (see CategorizedCorpusReader). Since many corpora allow rows that contain more than one sentence, it is possible to specify a sentence tokenizer to retrieve all sentences instead than all rows.nltk.corpus.reader.CategorizedTaggedCorpusReader - A reader for part-of-speech tagged corpora whose documents are divided into categories based on their file identifiers.nltk.corpus.reader.Pl196xCorpusReader - No class docstring; 0/3 instance variable, 0/1 class variable, 1/14 method documentednltk.corpus.reader.ProsConsCorpusReader - Reader for the Pros and Cons sentence dataset.nltk.corpus.reader.comparative_sents.Comparison - A Comparison represents a comparative sentence and its constituents.nltk.corpus.reader.conll.ConllSRLInstance - An SRL instance from a CoNLL corpus, which identifies and providing labels for the arguments of a single verb.nltk.corpus.reader.CorpusReader - A base class for "corpus reader" classes, each of which can be used to read a specific corpus format. Each individual corpus reader instance is used to read a specific corpus, consisting of one or more files under a common root directory...nltk.corpus.reader.AlignedCorpusReader - Reader for corpora of word-aligned sentences. Tokens are assumed to be separated by whitespace. Sentences begin on separate lines.nltk.corpus.reader.CategorizedSentencesCorpusReader - A reader for corpora in which each row represents a single instance, mainly a sentence. Istances are divided into categories based on their file identifiers (see CategorizedCorpusReader). Since many corpora allow rows that contain more than one sentence, it is possible to specify a sentence tokenizer to retrieve all sentences instead than all rows.nltk.corpus.reader.ChasenCorpusReader - Undocumentednltk.corpus.reader.ChunkedCorpusReader - Reader for chunked (and optionally tagged) corpora. Paragraphs are split using a block reader. They are then tokenized into sentences using a sentence tokenizer. Finally, these sentences are parsed into chunk trees using a string-to-chunktree conversion function...nltk.corpus.reader.CMUDictCorpusReader - No class docstring; 4/4 methods documentednltk.corpus.reader.ComparativeSentencesCorpusReader - Reader for the Comparative Sentence Dataset by Jindal and Liu (2006).nltk.corpus.reader.ConllCorpusReader - A corpus reader for CoNLL-style files. These files consist of a series of sentences, separated by blank lines. Each sentence is encoded using a table (or "grid") of values, where each line corresponds to a single word, and each column corresponds to an annotation type...nltk.corpus.reader.ConllChunkCorpusReader - A ConllCorpusReader whose data file contains three columns: words, pos, and chunk.nltk.corpus.reader.CrubadanCorpusReader - A corpus reader used to access language An Crubadan n-gram files.nltk.corpus.reader.IEERCorpusReader - No summarynltk.corpus.reader.IndianCorpusReader - List of words, one per line. Blank lines are ignored.nltk.corpus.reader.IPIPANCorpusReader - Corpus reader designed to work with corpus created by IPI PAN. See http://korpus.pl/en/ for more details about IPI PAN corpus.nltk.corpus.reader.LinThesaurusCorpusReader - Wrapper for the LISP-formatted thesauruses distributed by Dekang Lin.nltk.corpus.reader.NombankCorpusReader - Corpus reader for the nombank corpus, which augments the Penn Treebank with information about the predicate argument structure of every noun instance. The corpus consists of two parts: the predicate-argument annotations themselves, and a set of "frameset files" which define the argument labels used by the annotations, on a per-noun basis...nltk.corpus.reader.PanLexLiteCorpusReader - No class docstring; 0/3 instance variable, 0/2 constant, 3/4 methods documentednltk.corpus.reader.PlaintextCorpusReader - Reader for corpora that consist of plaintext documents. Paragraphs are assumed to be split using blank lines. Sentences and words can be tokenized using the default tokenizers, or by custom tokenizers specificed as parameters to the constructor.nltk.corpus.reader.CategorizedPlaintextCorpusReader - A reader for plaintext corpora whose documents are divided into categories based on their file identifiers.nltk.corpus.reader.EuroparlCorpusReader - Reader for Europarl corpora that consist of plaintext documents. Documents are divided into chapters instead of paragraphs as for regular plaintext documents. Chapters are separated using blank lines. ...nltk.corpus.reader.UdhrCorpusReader - Undocumentednltk.corpus.reader.PPAttachmentCorpusReader - sentence_id verb noun1 preposition noun2 attachmentnltk.corpus.reader.PropbankCorpusReader - Corpus reader for the propbank corpus, which augments the Penn Treebank with information about the predicate argument structure of every verb instance. The corpus consists of two parts: the predicate-argument annotations themselves, and a set of "frameset files" which define the argument labels used by the annotations, on a per-verb basis...nltk.corpus.reader.ProsConsCorpusReader - Reader for the Pros and Cons sentence dataset.nltk.corpus.reader.ReviewsCorpusReader - Reader for the Customer Review Data dataset by Hu, Liu (2004). Note: we are not applying any sentence tokenization at the moment, just word tokenization.nltk.corpus.reader.SensevalCorpusReader - No class docstring; 1/3 method documentednltk.corpus.reader.SentiWordNetCorpusReader - No class docstring; 0/1 instance variable, 1/5 method documentednltk.corpus.reader.StringCategoryCorpusReader - No class docstring; 0/1 instance variable, 2/4 methods documentednltk.corpus.reader.SwitchboardCorpusReader - Undocumentednltk.corpus.reader.SyntaxCorpusReader - An abstract base class for reading corpora consisting of syntactically parsed text. Subclasses should define:nltk.corpus.reader.BracketParseCorpusReader - Reader for corpora that consist of parenthesis-delineated parse trees, like those found in the "combined" section of the Penn Treebank, e.g. "(S (NP (DT the) (JJ little) (NN dog)) (VP (VBD barked)))".nltk.corpus.reader.AlpinoCorpusReader - Reader for the Alpino Dutch Treebank. This corpus has a lexical breakdown structure embedded, as read by _parse Unfortunately this puts punctuation and some other words out of the sentence order in the xml element tree...nltk.corpus.reader.CategorizedBracketParseCorpusReader - A reader for parsed corpora whose documents are divided into categories based on their file identifiers. @author: Nathan Schneider <nschneid@cs.cmu.edu>nltk.corpus.reader.DependencyCorpusReader - No class docstring; 1/7 method documentednltk.corpus.reader.KNBCorpusReader - __init__, which specifies the location of the corpus and a method for detecting the sentence blocks in corpus files.nltk.corpus.reader.SinicaTreebankCorpusReader - Reader for the sinica treebank.nltk.corpus.reader.TaggedCorpusReader - Reader for simple part-of-speech tagged corpora. Paragraphs are assumed to be split using blank lines. Sentences and words can be tokenized using the default tokenizers, or by custom tokenizers specified as parameters to the constructor...nltk.corpus.reader.CategorizedTaggedCorpusReader - A reader for part-of-speech tagged corpora whose documents are divided into categories based on their file identifiers.nltk.corpus.reader.MacMorphoCorpusReader - A corpus reader for the MAC_MORPHO corpus. Each line contains a single tagged word, using '_' as a separator. Sentence boundaries are based on the end-sentence tag ('_.'). Paragraph information is not included in the corpus, so each paragraph returned by ...nltk.corpus.reader.MTECorpusReader - Reader for corpora following the TEI-p5 xml scheme, such as MULTEXT-East. MULTEXT-East contains part-of-speech-tagged words with a quite precise tagging scheme. These tags can be converted to the Universal tagset...nltk.corpus.reader.TimitTaggedCorpusReader - A corpus reader for tagged sentences that are included in the TIMIT corpus.nltk.corpus.reader.TimitCorpusReader - Reader for the TIMIT corpus (or any other corpus with the same file layout and use of file formats). The corpus root directory should contain the following files:nltk.corpus.reader.ToolboxCorpusReader - Undocumentednltk.corpus.reader.TwitterCorpusReader - Reader for corpora that consist of Tweets represented as a list of line-delimited JSON.nltk.corpus.reader.WordListCorpusReader - List of words, one per line. Blank lines are ignored.nltk.corpus.reader.MWAPPDBCorpusReader - This class is used to read the list of word pairs from the subset of lexical pairs of The Paraphrase Database (PPDB) XXXL used in the Monolingual Word Alignment (MWA) algorithm described in Sultan et al...nltk.corpus.reader.NonbreakingPrefixesCorpusReader - This is a class to read the nonbreaking prefixes textfiles from the Moses Machine Translation toolkit. These lists are used in the Python port of the Moses' word tokenizer.nltk.corpus.reader.OpinionLexiconCorpusReader - Reader for Liu and Hu opinion lexicon. Blank lines and readme are ignored.nltk.corpus.reader.PanlexSwadeshCorpusReader - This is a class to read the PanLex Swadesh list fromnltk.corpus.reader.SwadeshCorpusReader - No class docstring; 1/1 method documentednltk.corpus.reader.UnicharsCorpusReader - This class is used to read lists of characters from the Perl Unicode Properties (see http://perldoc.perl.org/perluniprops.html). The files in the perluniprop.zip are extracted using the Unicode::Tussle module from http://search...nltk.corpus.reader.WordNetCorpusReader - A corpus reader used to access wordnet or its variants.nltk.corpus.reader.WordNetICCorpusReader - A corpus reader for the WordNet information content corpus.nltk.corpus.reader.XMLCorpusReader - Corpus reader for corpora whose documents are xml files.nltk.corpus.reader.BNCCorpusReader - Corpus reader for the XML version of the British National Corpus.nltk.corpus.reader.CHILDESCorpusReader - Corpus reader for the XML version of the CHILDES corpus. The CHILDES corpus is available at ``https://childes.talkbank.org/``. The XML version of CHILDES is located at ``https://childes.talkbank.org/data-xml/``...nltk.corpus.reader.FramenetCorpusReader - A corpus reader for the Framenet Corpus.nltk.corpus.reader.NKJPCorpusReader - No class docstring; 0/1 instance variable, 0/4 constant, 9/10 methods documentednltk.corpus.reader.NPSChatCorpusReader - Undocumentednltk.corpus.reader.Pl196xCorpusReader - No class docstring; 0/3 instance variable, 0/1 class variable, 1/14 method documentednltk.corpus.reader.RTECorpusReader - Corpus reader for corpora in RTE challenges.nltk.corpus.reader.SemcorCorpusReader - Corpus reader for the SemCor Corpus. For access to the complete XML data structure, use the ``xml()`` method. For access to simple word lists and tagged word lists, use ``words()``, ``sents()``, ``tagged_words()``, and ``tagged_sents()``.nltk.corpus.reader.VerbnetCorpusReader - An NLTK interface to the VerbNet verb lexicon.nltk.corpus.reader.YCOECorpusReader - Corpus reader for the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE), a 1.5 million word syntactically-annotated corpus of Old English prose texts.nltk.corpus.reader.framenet.Future - Wraps and acts as a proxy for a value to be loaded lazily (on demand). Adapted from https://gist.github.com/sergey-miryanov/2935416nltk.corpus.reader.ieer.IEERDocument - Undocumentednltk.corpus.reader.nombank.NombankInstance - No class docstring; 1/1 property, 9/9 instance variables, 0/1 class variable, 0/4 method, 0/1 static method documentednltk.corpus.reader.nombank.NombankPointer - A pointer used by nombank to identify one or more constituents in a parse tree. NombankPointer is an abstract base class with three concrete subclasses:nltk.corpus.reader.nombank.NombankChainTreePointer - No class docstring; 1/1 instance variable, 0/4 method documentednltk.corpus.reader.nombank.NombankSplitTreePointer - No class docstring; 1/1 instance variable, 0/4 method documentednltk.corpus.reader.nombank.NombankTreePointer - wordnum:height*wordnum:height*... wordnum:height,nltk.corpus.reader.ppattach.PPAttachment - Undocumentednltk.corpus.reader.propbank.PropbankInflection - Undocumentednltk.corpus.reader.propbank.PropbankInstance - No class docstring; 3/3 properties, 9/9 instance variables, 0/1 class variable, 0/4 method, 0/1 static method documentednltk.corpus.reader.propbank.PropbankPointer - A pointer used by propbank to identify one or more constituents in a parse tree. PropbankPointer is an abstract base class with three concrete subclasses:nltk.corpus.reader.propbank.PropbankChainTreePointer - No class docstring; 1/1 instance variable, 0/4 method documentednltk.corpus.reader.propbank.PropbankSplitTreePointer - No class docstring; 1/1 instance variable, 0/4 method documentednltk.corpus.reader.propbank.PropbankTreePointer - wordnum:height*wordnum:height*... wordnum:height,nltk.corpus.reader.reviews.Review - A Review is the main block of a ReviewsCorpusReader.nltk.corpus.reader.reviews.ReviewLine - A ReviewLine represents a sentence of the review, together with (optional) annotations of its features and notes about the reviewed item.nltk.corpus.reader.rte.RTEPair - Container for RTE text-hypothesis pairs.nltk.corpus.reader.senseval.SensevalInstance - Undocumentednltk.corpus.reader.SentiSynset - No class docstring; 0/4 instance variable, 1/6 method documentednltk.corpus.reader.timit.SpeakerInfo - Undocumentednltk.corpus.reader.wordnet._WordNetObject - A common base class for lemmas and synsets.nltk.corpus.reader.wordnet.Lemma - The lexical entry for a single morphological form of a sense-disambiguated word.nltk.corpus.reader.wordnet.Synset - Create a Synset from a "<lemma>.<pos>.<number>" string where: <lemma> is the word's morphological stem <pos> is one of the module attributes ADJ, ADJ_SAT, ADV, NOUN or VERB <number> is the sense number, counting from 0.nltk.corpus.util.LazyCorpusLoader - To see the API documentation for this lazily loaded corpus, first run corpus.ensure_loaded(), and then run help(this_corpus).nltk.downloader.Collection - A directory entry for a collection of downloadable packages. These entries are extracted from the XML index file that is downloaded by Downloader.nltk.downloader.Downloader - A class used to access the NLTK data server, which can be used to download corpora and other data packages.nltk.downloader.DownloaderGUI - Graphical interface for downloading packages from the NLTK data server.nltk.downloader.DownloaderMessage - A status message object, used by incr_download to communicate its progress.nltk.downloader.ErrorMessage - Data server encountered an errornltk.downloader.FinishCollectionMessage - Data server has finished working on a collection of packages.nltk.downloader.FinishDownloadMessage - Data server has finished downloading a package.nltk.downloader.FinishPackageMessage - Data server has finished working on a package.nltk.downloader.FinishUnzipMessage - Data server has finished unzipping a package.nltk.downloader.ProgressMessage - Indicates how much progress the data server has madenltk.downloader.SelectDownloadDirMessage - Indicates what download directory the data server is usingnltk.downloader.StaleMessage - The package download file is out-of-date or corruptnltk.downloader.StartCollectionMessage - Data server has started working on a collection of packages.nltk.downloader.StartDownloadMessage - Data server has started downloading a package.nltk.downloader.StartPackageMessage - Data server has started working on a package.nltk.downloader.StartUnzipMessage - Data server has started unzipping a package.nltk.downloader.UpToDateMessage - The package download file is already up-to-datenltk.downloader.DownloaderShell - Undocumentednltk.downloader.Package - A directory entry for a downloadable package. These entries are extracted from the XML index file that is downloaded by Downloader. Each package consists of a single file; but if that file is a zip file, then it can be automatically decompressed when the package is installed.nltk.draw.cfg.CFGDemo - Undocumentednltk.draw.cfg.CFGEditor - A dialog window for creating and editing context free grammars. CFGEditor imposes the following restrictions:nltk.draw.table.Table - A display widget for a table of values, based on a MultiListbox widget. For many purposes, Table can be treated as a list-of-lists. E.g., table[i] is a list of the values for row i; and table.append(row) adds a new row with the given lits of values...nltk.draw.tree.TreeView - No class docstring; 0/6 instance variable, 1/6 method documentednltk.draw.util.CanvasFrame - A Tkinter frame containing a canvas and scrollbars. CanvasFrame uses a ScrollWatcherWidget to ensure that all of the canvas widgets contained on its canvas are within its scrollregion. In order for CanvasFrame...nltk.draw.util.ColorizedList - An abstract base class for displaying a colorized list of items. Subclasses should define:nltk.app.chartparser_app.EdgeList - Undocumentednltk.draw.cfg.ProductionList - Undocumentednltk.draw.util.EntryDialog - A dialog box for enteringnltk.draw.util.ShowText - A Tkinter window used to display a text. ShowText is typically used by graphical tools to display help text, or similar information.nltk.featstruct._UnificationFailure - Undocumentednltk.featstruct.CustomFeatureValue - An abstract base class for base values that define a custom unification method. The custom unification method of CustomFeatureValue will be used during unification if:nltk.featstruct.FeatStructReader - No class docstring; 0/7 instance variable, 0/12 constant, 5/20 methods documentednltk.featstruct.Feature - A feature identifier that's specialized to put additional constraints, default values, etc.nltk.featstruct.RangeFeature - Undocumentednltk.featstruct.SlashFeature - Undocumentednltk.grammar.CFG - A context-free grammar. A grammar consists of a start state and a set of productions. The set of terminals and nonterminals is implicitly specified by the productions.nltk.grammar.FeatureGrammar - A feature-based grammar. This is equivalent to a CFG whose nonterminals are all FeatStructNonterminal.nltk.grammar.PCFG - A probabilistic context-free grammar. A PCFG consists of a start state and a set of productions with probabilities. The set of terminals and nonterminals is implicitly specified by the productions.nltk.grammar.DependencyGrammar - A dependency grammar. A DependencyGrammar consists of a set of productions. Each production specifies a head/modifier relationship between a pair of words.nltk.grammar.FeatureValueType - A helper class for FeatureGrammars, designed to be different from ordinary strings. This is to stop the FeatStruct FOO[] from being compare equal to the terminal "FOO".nltk.grammar.Nonterminal - A non-terminal symbol for a context free grammar. Nonterminal is a wrapper class for node values; it is used by Production objects to distinguish node values from leaf values. The node value that is wrapped by a ...nltk.grammar.FeatStructNonterminal - A feature structure that's also a nonterminal. It acts as its own symbol, and automatically freezes itself when hashed.nltk.grammar.ProbabilisticDependencyGrammar - No summarynltk.grammar.Production - A grammar production. Each production maps a single symbol on the "left-hand side" to a sequence of symbols on the "right-hand side". (In the case of context-free productions, the left-hand side must be a ...nltk.grammar.DependencyProduction - A dependency grammar production. Each production maps a single head word to an unordered list of one or more modifier words.nltk.grammar.ProbabilisticProduction - A probabilistic context free grammar production. A PCFG ProbabilisticProduction is essentially just a Production that has an associated probability, which represents how likely it is that this production will be used...nltk.inference.discourse.DiscourseTester - Check properties of an ongoing discourse.nltk.inference.nonmonotonic.PredHolder - This class will be used by a dictionary that will store information about predicates to be used by the ClosedWorldProver.nltk.inference.prover9.Prover9CommandParent - A common base class used by both Prover9Command and MaceCommand, which is responsible for maintaining a goal and a set of assumptions, and generating prover9-style input files from them.nltk.inference.mace.MaceCommand - A MaceCommand specific to the Mace model builder. It contains a print_assumptions() method that is used to print the list of assumptions in multiple formats.nltk.inference.prover9.Prover9Command - A ProverCommand specific to the Prover9 prover. It contains the a print_assumptions() method that is used to print the list of assumptions in multiple formats.nltk.inference.prover9.Prover9Parent - A common class extended by both Prover9 and Mace <mace.Mace>. It contains the functionality required to convert NLTK-style expressions into Prover9-style expressions.nltk.inference.mace.Mace - No class docstring; 1/2 instance variable, 2/3 methods documentednltk.inference.prover9.Prover9 - No class docstring; 1/3 instance variable, 4/5 methods documentednltk.inference.resolution.BindingDict - No class docstring; 0/1 instance variable, 4/8 methods documentednltk.inference.resolution.DebugObject - Undocumentednltk.inference.tableau.Agenda - No class docstring; 0/1 instance variable, 1/12 method documentednltk.inference.tableau.Categories - Undocumentednltk.inference.tableau.Debug - Undocumentednltk.internals.Deprecated - A base class used to mark deprecated classes. A typical usage is to alert users that the name of a class has changed:nltk.internals.ElementWrapper - A wrapper around ElementTree Element objects whose main purpose is to provide nicer __repr__ and __str__ methods. In addition, any of the wrapped Element's methods that return other Element objects are overridden to wrap those values before returning them.nltk.metrics.agreement.AnnotationTask - Represents an annotation task, i.e. people assign labels to items.nltk.metrics.association.ContingencyMeasures - Wraps NgramAssocMeasures classes such that the arguments of association measures are contingency table values rather than marginals.nltk.metrics.confusionmatrix.ConfusionMatrix - The confusion matrix between a list of reference values and a corresponding list of test values. Entry [r,t] of this matrix is a count of the number of times that the reference value r corresponds to the test value ...nltk.metrics.paice.Paice - Class for storing lemmas, stems and evaluation metrics.nltk.misc.minimalset.MinimalSet - Find contexts where more than one possible target value can appear. E.g. if targets are word-initial letters, and contexts are the remainders of words, then we would like to find cases like "fat" vs "cat", and "training" vs "draining"...nltk.parse.api.ParserI - A processing class for deriving trees that represent possible structures for a sequence of tokens. These tree structures are known as "parses". Typically, parsers are used to derive syntax trees for sentences...nltk.parse.bllip.BllipParser - Interface for parsing with BLLIP Parser. BllipParser objects can be constructed with the BllipParser.from_unified_model_dir class method or manually using the BllipParser constructor.nltk.parse.chart.ChartParser - A generic chart parser. A "strategy", or list of ChartRuleI instances, is used to decide what edges to add to the chart. In particular, ChartParser uses the following algorithm to parse texts:nltk.parse.chart.BottomUpChartParser - A ChartParser using a bottom-up parsing strategy. See ChartParser for more information.nltk.parse.chart.BottomUpLeftCornerChartParser - A ChartParser using a bottom-up left-corner parsing strategy. This strategy is often more efficient than standard bottom-up. See ChartParser for more information.nltk.parse.chart.LeftCornerChartParser - Undocumentednltk.parse.chart.SteppingChartParser - A ChartParser that allows you to step through the parsing process, adding a single edge at a time. It also allows you to change the parser's strategy or grammar midway through parsing a text.nltk.parse.chart.TopDownChartParser - A ChartParser using a top-down parsing strategy. See ChartParser for more information.nltk.parse.earleychart.IncrementalChartParser - An incremental chart parser implementing Jay Earley's parsing algorithm:nltk.parse.earleychart.EarleyChartParser - Undocumentednltk.parse.earleychart.FeatureIncrementalChartParser - Undocumentednltk.parse.earleychart.FeatureEarleyChartParser - Undocumentednltk.parse.earleychart.FeatureIncrementalBottomUpChartParser - Undocumentednltk.parse.earleychart.FeatureIncrementalTopDownChartParser - Undocumentednltk.parse.earleychart.IncrementalBottomUpChartParser - Undocumentednltk.parse.earleychart.IncrementalLeftCornerChartParser - Undocumentednltk.parse.earleychart.IncrementalTopDownChartParser - Undocumentednltk.parse.featurechart.FeatureChartParser - Undocumentednltk.parse.earleychart.FeatureIncrementalChartParser - Undocumentednltk.parse.earleychart.FeatureEarleyChartParser - Undocumentednltk.parse.earleychart.FeatureIncrementalBottomUpChartParser - Undocumentednltk.parse.earleychart.FeatureIncrementalTopDownChartParser - Undocumentednltk.parse.featurechart.FeatureBottomUpChartParser - Undocumentednltk.parse.featurechart.FeatureBottomUpLeftCornerChartParser - Undocumentednltk.parse.featurechart.FeatureTopDownChartParser - Undocumentednltk.parse.corenlp.GenericCoreNLPParser - Interface to the CoreNLP Parser.nltk.parse.corenlp.CoreNLPDependencyParser - Dependency parser.nltk.parse.corenlp.CoreNLPParser - No summarynltk.parse.malt.MaltParser - A class for dependency parsing with MaltParser. The input is the paths to: - a maltparser directory - (optionally) the path to a pre-trained MaltParser .mco model file - (optionally) the tagger to use for POS tagging before parsing - (optionally) additional Java arguments...nltk.parse.pchart.BottomUpProbabilisticChartParser - An abstract bottom-up parser for PCFG grammars that uses a Chart to record partial results. BottomUpProbabilisticChartParser maintains a queue of edges that can be added to the chart. This queue is initialized with edges for each token in the text that is being parsed...nltk.parse.pchart.InsideChartParser - A bottom-up parser for PCFG grammars that tries edges in descending order of the inside probabilities of their trees. The "inside probability" of a tree is simply the probability of the entire tree, ignoring its context...nltk.parse.pchart.LongestChartParser - A bottom-up parser for PCFG grammars that tries longer edges before shorter ones. This sorting order results in a type of best-first search strategy.nltk.parse.pchart.RandomChartParser - A bottom-up parser for PCFG grammars that tries edges in random order. This sorting order results in a random search strategy.nltk.parse.pchart.UnsortedChartParser - A bottom-up parser for PCFG grammars that tries edges in whatever order.nltk.parse.recursivedescent.RecursiveDescentParser - A simple top-down CFG parser that parses texts by recursively expanding the fringe of a Tree, and matching it against a text.nltk.parse.recursivedescent.SteppingRecursiveDescentParser - A RecursiveDescentParser that allows you to step through the parsing process, performing a single operation at a time.nltk.parse.shiftreduce.ShiftReduceParser - A simple bottom-up CFG parser that uses two operations, "shift" and "reduce", to find a single parse for a text.nltk.parse.shiftreduce.SteppingShiftReduceParser - A ShiftReduceParser that allows you to setp through the parsing process, performing a single operation at a time. It also allows you to change the parser's grammar midway through parsing a text.nltk.parse.stanford.GenericStanfordParser - Interface to the Stanford Parsernltk.parse.stanford.StanfordDependencyParser - No summarynltk.parse.stanford.StanfordNeuralDependencyParser - No summarynltk.parse.stanford.StanfordParser - No summarynltk.parse.viterbi.ViterbiParser - A bottom-up PCFG parser that uses dynamic programming to find the single most likely parse for a text. The ViterbiParser parser parses texts by filling in a "most likely constituent table". This table records the most probable tree representation for any given span and node value...nltk.parse.chart.Chart - A blackboard for hypotheses about the syntactic constituents of a sentence. A chart contains a set of edges, and each edge encodes a single hypothesis about the structure of some portion of the sentence.nltk.ccg.chart.CCGChart - Undocumentednltk.parse.earleychart.IncrementalChart - Undocumentednltk.parse.earleychart.FeatureIncrementalChart - Undocumentednltk.parse.featurechart.FeatureChart - A Chart for feature grammars. :see: Chart for more information.nltk.parse.earleychart.FeatureIncrementalChart - Undocumentednltk.parse.featurechart.InstantiateVarsChart - A specialized chart that 'instantiates' variables whose names start with '@', by replacing them with unique new variables. In particular, whenever a complete edge is added to the chart, any variables in the edge's ...nltk.parse.chart.ChartRuleI - A rule that specifies what new edges are licensed by any given set of existing edges. Each chart rule expects a fixed number of edges, as indicated by the class variable NUM_EDGES. In particular:nltk.parse.chart.AbstractChartRule - An abstract base class for chart rules. AbstractChartRule provides:nltk.ccg.chart.BackwardTypeRaiseRule - Class for applying backward type raising.nltk.ccg.chart.BinaryCombinatorRule - Class implementing application of a binary combinator to a chart. Takes the directed combinator to apply.nltk.ccg.chart.ForwardTypeRaiseRule - Class for applying forward type raisingnltk.parse.chart.BottomUpPredictRule - A rule licensing any edge corresponding to a production whose right-hand side begins with a complete edge's left-hand side. In particular, this rule specifies that [A -> alpha \*] licenses the edge [B -> \* A beta]...nltk.app.chartparser_app.BottomUpEdgeRule - Undocumentednltk.parse.chart.BottomUpPredictCombineRule - A rule licensing any edge corresponding to a production whose right-hand side begins with a complete edge's left-hand side. In particular, this rule specifies that [A -> alpha \*] licenses the edge [B -> A \* beta]...nltk.app.chartparser_app.BottomUpLeftCornerEdgeRule - Undocumentednltk.parse.chart.FilteredBottomUpPredictCombineRule - Undocumentednltk.parse.featurechart.FeatureBottomUpPredictCombineRule - Undocumentednltk.parse.featurechart.FeatureBottomUpPredictRule - Undocumentednltk.parse.chart.EmptyPredictRule - A rule that inserts all empty productions as passive edges, in every position in the chart.nltk.parse.featurechart.FeatureEmptyPredictRule - Undocumentednltk.parse.chart.FundamentalRule - A rule that joins two adjacent edges to form a single combined edge. In particular, this rule specifies that any pair of edgesnltk.parse.chart.SingleEdgeFundamentalRule - A rule that joins a given edge with adjacent edges in the chart, to form combined edges. In particular, this rule specifies that either of the edges:nltk.app.chartparser_app.FundamentalEdgeRule - Undocumentednltk.parse.chart.FilteredSingleEdgeFundamentalRule - Undocumentednltk.parse.earleychart.FilteredCompleteFundamentalRule - Undocumentednltk.parse.earleychart.CompleteFundamentalRule - Undocumentednltk.parse.earleychart.CompleterRule - Undocumentednltk.parse.earleychart.FeatureCompleterRule - Undocumentednltk.parse.earleychart.ScannerRule - Undocumentednltk.parse.earleychart.FeatureScannerRule - Undocumentednltk.parse.featurechart.FeatureSingleEdgeFundamentalRule - A specialized version of the completer / single edge fundamental rule that operates on nonterminals whose symbols are ``FeatStructNonterminal``s. Rather than simply comparing the nonterminals for equality, they are unified.nltk.parse.earleychart.FeatureCompleteFundamentalRule - Undocumentednltk.parse.featurechart.FeatureFundamentalRule - Broken description
nltk.parse.chart.LeafInitRule - Undocumentednltk.parse.chart.TopDownInitRule - A rule licensing edges corresponding to the grammar productions for the grammar's start symbol. In particular, this rule specifies that [S -> \* alpha][0:i] is licensed for each grammar production S -> alpha...nltk.parse.featurechart.FeatureTopDownInitRule - Undocumentednltk.parse.chart.TopDownPredictRule - A rule licensing edges corresponding to the grammar productions for the nonterminal following an incomplete edge's dot. In particular, this rule specifies that [A -> alpha \* B beta][i:j] licenses the edge ...nltk.app.chartparser_app.TopDownPredictEdgeRule - Undocumentednltk.parse.chart.CachedTopDownPredictRule - A cached version of TopDownPredictRule. After the first time this rule is applied to an edge with a given end and next, it will not generate any more edges for edges with that end and next.nltk.parse.earleychart.PredictorRule - Undocumentednltk.parse.featurechart.FeatureTopDownPredictRule - A specialized version of the (cached) top down predict rule that operates on nonterminals whose symbols are ``FeatStructNonterminal``s. Rather than simply comparing the nonterminals for equality, they are unified.nltk.parse.earleychart.FeaturePredictorRule - Undocumentednltk.parse.pchart.ProbabilisticBottomUpInitRule - Undocumentednltk.parse.pchart.ProbabilisticBottomUpPredictRule - Undocumentednltk.parse.pchart.ProbabilisticFundamentalRule - Undocumentednltk.parse.pchart.SingleEdgeProbabilisticFundamentalRule - Undocumentednltk.parse.chart.EdgeI - A hypothesis about the structure of part of a sentence. Each edge records the fact that a structure is (partially) consistent with the sentence. An edge contains:nltk.ccg.chart.CCGEdge - Undocumentednltk.ccg.chart.CCGLeafEdge - Class representing leaf edges in a CCG derivation.nltk.parse.chart.LeafEdge - An edge that records the fact that a leaf value is consistent with a word in the sentence. A leaf edge consists of:nltk.parse.pchart.ProbabilisticLeafEdge - Undocumentednltk.parse.chart.TreeEdge - An edge that records the fact that a tree is (partially) consistent with the sentence. A tree edge consists of:nltk.parse.featurechart.FeatureTreeEdge - A specialized tree edge that allows shared variable bindings between nonterminals on the left-hand side and right-hand side.nltk.parse.pchart.ProbabilisticTreeEdge - Undocumentednltk.parse.corenlp.CoreNLPServer - No class docstring; 0/6 instance variable, 0/2 constant, 1/5 method documentednltk.parse.dependencygraph.DependencyGraph - A container for the nodes and labelled edges of a dependency structure.nltk.parse.evaluate.DependencyEvaluator - Class for measuring labelled and unlabelled attachment score for dependency parsing. Note that the evaluation ignores punctuation.nltk.parse.nonprojectivedependencyparser.DependencyScorerI - A scorer for calculated the weights on the edges of a weighted dependency graph. This is used by a ProbabilisticNonprojectiveParser to initialize the edge weights of a DependencyGraph. While typically this would be done by training a binary classifier, any class that can return a multidimensional list representation of the edge weights can implement this interface...nltk.parse.nonprojectivedependencyparser.DemoScorer - Undocumentednltk.parse.nonprojectivedependencyparser.NaiveBayesDependencyScorer - A dependency scorer built around a MaxEnt classifier. In this particular class that classifier is a NaiveBayesClassifier. It uses head-word, head-tag, child-word, and child-tag features for classification.nltk.parse.nonprojectivedependencyparser.NonprojectiveDependencyParser - A non-projective, rule-based, dependency parser. This parser will return the set of all possible non-projective parses based on the word-to-word relations defined in the parser's dependency grammar, and will allow the branches of the parse tree to cross in order to capture a variety of linguistic phenomena that a projective parser will not.nltk.parse.nonprojectivedependencyparser.ProbabilisticNonprojectiveParser - A probabilistic non-projective dependency parser.nltk.parse.projectivedependencyparser.ChartCell - A cell from the parse chart formed when performing the CYK algorithm. Each cell keeps track of its x and y coordinates (though this will probably be discarded), and a list of spans serving as the cell's entries.nltk.parse.projectivedependencyparser.DependencySpan - A contiguous span over some part of the input string representing dependency (head -> modifier) relationships amongst words. An atomic span corresponds to only one word so it isn't a 'span' in the conventional sense, as its _start_index = _end_index = _head_index for concatenation purposes...nltk.parse.projectivedependencyparser.ProbabilisticProjectiveDependencyParser - A probabilistic, projective dependency parser.nltk.parse.projectivedependencyparser.ProjectiveDependencyParser - A projective, rule-based, dependency parser. A ProjectiveDependencyParser is created with a DependencyGrammar, a set of productions specifying word-to-word dependency relations. The parse() method will then return the set of all parses, in tree representation, for a given input sequence of tokens...nltk.parse.transitionparser.Configuration - Class for holding configuration which is the partial analysis of the input sentence. The transition based parser aims at finding set of operators that transfer the initial configuration to the terminal configuration.nltk.parse.transitionparser.Transition - This class defines a set of transition which is applied to a configuration to get another configuration Note that for different parsing algorithm, the transition is different.nltk.parse.util.TestGrammar - Unit tests for CFG.nltk.probability.ProbabilisticMixIn - A mix-in class to associate probabilities with other classes (trees, rules, etc.). To use the ProbabilisticMixIn class, define a new class that derives from an existing class and from ProbabilisticMixIn...nltk.probability.ImmutableProbabilisticMixIn - Undocumentednltk.grammar.ProbabilisticProduction - A probabilistic context free grammar production. A PCFG ProbabilisticProduction is essentially just a Production that has an associated probability, which represents how likely it is that this production will be used...nltk.tree.ImmutableProbabilisticTree - Undocumentednltk.tree.ProbabilisticTree - Undocumentednltk.sem.boxer.AbstractBoxerDrs - No class docstring; 2/8 methods documentednltk.sem.boxer.BoxerDrs - Undocumentednltk.sem.boxer.BoxerIndexed - Undocumentednltk.sem.boxer.BoxerCard - Undocumentednltk.sem.boxer.BoxerEq - Undocumentednltk.sem.boxer.BoxerNamed - Undocumentednltk.sem.boxer.BoxerOr - Undocumentednltk.sem.boxer.BoxerPred - Undocumentednltk.sem.boxer.BoxerProp - Undocumentednltk.sem.boxer.BoxerRel - Undocumentednltk.sem.boxer.BoxerWhq - Undocumentednltk.sem.boxer.BoxerNot - Undocumentednltk.sem.boxer.Boxer - This class is an interface to Johan Bos's program Boxer, a wide-coverage semantic parser that produces Discourse Representation Structures (DRSs).nltk.sem.boxer.NltkDrtBoxerDrsInterpreter - No class docstring; 0/1 instance variable, 1/4 method documentednltk.sem.boxer.PassthroughBoxerDrsInterpreter - Undocumentednltk.sem.chat80.Concept - A Concept class, loosely based on SKOS (http://www.w3.org/TR/swbp-skos-core-guide/).nltk.sem.cooper_storage.CooperStore - A container for handling quantifier ambiguity via Cooper storage.nltk.sem.drt.DrsDrawer - No class docstring; 0/3 instance variable, 0/3 constant, 8/15 methods documentednltk.sem.drt.DrtExpression - This is the base abstract DRT Expression from which every DRT Expression extends.nltk.sem.drt.DRS - A Discourse Representation Structure.nltk.sem.drt.DrtAbstractVariableExpression - No class docstring; 1/4 method documentednltk.sem.drt.DrtConstantExpression - Undocumentednltk.sem.drt.DrtFunctionVariableExpression - Undocumentednltk.sem.drt.DrtIndividualVariableExpression - Undocumentednltk.sem.drt.DrtEventVariableExpression - Undocumentednltk.sem.drt.DrtApplicationExpression - No class docstring; 1/3 method documentednltk.sem.drt.DrtBinaryExpression - No class docstring; 1/3 method, 0/1 static method documentednltk.sem.drt.DrtBooleanExpression - Undocumentednltk.sem.drt.DrtConcatenation - DRS of the form '(DRS + DRS)'nltk.sem.drt.DrtOrExpression - Undocumentednltk.sem.drt.DrtEqualityExpression - Undocumentednltk.sem.drt.DrtLambdaExpression - No class docstring; 1/3 method documentednltk.sem.drt.DrtNegatedExpression - No class docstring; 1/3 method documentednltk.sem.drt.DrtProposition - No class docstring; 0/2 instance variable, 2/11 methods documentednltk.sem.drt.PossibleAntecedents - No class docstring; 2/4 methods documentednltk.sem.drt_glue_demo.DrsWidget - Undocumentednltk.sem.drt_glue_demo.DrtGlueDemo - No class docstring; 0/28 instance variable, 1/26 method documentednltk.sem.evaluate.Model - A first order model is a domain D of discourse and a valuation V.nltk.sem.glue.Glue - No class docstring; 0/5 instance variable, 1/11 method documentednltk.sem.glue.DrtGlue - Undocumentednltk.sem.glue.GlueFormula - No class docstring; 0/3 instance variable, 2/12 methods documentednltk.sem.glue.DrtGlueFormula - Undocumentednltk.sem.hole.Constants - Undocumentednltk.sem.hole.Constraint - This class represents a constraint of the form (L =< N), where L is a label and N is a node (a label or a hole).nltk.sem.hole.HoleSemantics - This class holds the broken-down components of a hole semantics, i.e. it extracts the holes, labels, logic formula fragments and constraints out of a big conjunction of such as produced by the hole semantics grammar...nltk.sem.linearlogic.BindingDict - No class docstring; 0/1 instance variable, 4/9 methods documentednltk.sem.linearlogic.Expression - Undocumentednltk.sem.linearlogic.ApplicationExpression - No class docstring; 0/3 instance variable, 2/6 methods documentednltk.sem.linearlogic.AtomicExpression - No class docstring; 0/2 instance variable, 4/9 methods documentednltk.sem.linearlogic.ConstantExpression - No class docstring; 1/1 method documentednltk.sem.linearlogic.VariableExpression - No class docstring; 1/1 method documentednltk.sem.linearlogic.ImpExpression - No class docstring; 0/2 instance variable, 4/10 methods documentednltk.sem.linearlogic.Tokens - Undocumentednltk.sem.logic.LogicParser - A lambda calculus expression parser.nltk.sem.drt.DrtParser - A lambda calculus expression parser.nltk.sem.boxer.BoxerDrsParser - Reparse the str form of subclasses of AbstractBoxerDrsnltk.sem.boxer.BoxerOutputDrsParser - No class docstring; 0/3 instance variable, 4/31 methods documentednltk.sem.linearlogic.LinearLogicParser - A linear logic expression parser.nltk.sem.logic.SubstituteBindingsI - An interface for classes that can perform substitutions for variables.nltk.featstruct.FeatStruct - A mapping from feature identifiers to feature values, where each feature value is either a basic value (such as a string or an integer), or a nested feature structure. There are two types of feature structure:...nltk.featstruct.FeatDict - A feature structure that acts like a Python dictionary. I.e., a mapping from feature identifiers to feature values, where a feature identifier can be a string or a Feature; and where a feature value can be either a basic value (such as a string or an integer), or a nested feature structure...nltk.grammar.FeatStructNonterminal - A feature structure that's also a nonterminal. It acts as its own symbol, and automatically freezes itself when hashed.nltk.featstruct.FeatList - A list of feature values, where each feature value is either a basic value (such as a string or an integer), or a nested feature structure.nltk.featstruct.SubstituteBindingsSequence - A mixin class for sequence clases that distributes variables() and substitute_bindings() over the object's elements.nltk.featstruct.FeatureValueConcat - A base feature value that represents the concatenation of two or more FeatureValueTuple or Variable.nltk.featstruct.FeatureValueSet - A base feature value that is a set of other base feature values. FeatureValueSet implements SubstituteBindingsI, so it any variable substitutions will be propagated to the elements contained by the set...nltk.featstruct.FeatureValueTuple - A base feature value that is a tuple of other base feature values. FeatureValueTuple implements SubstituteBindingsI, so it any variable substitutions will be propagated to the elements contained by the set...nltk.featstruct.FeatureValueUnion - A base feature value that represents the union of two or more FeatureValueSet or Variable.nltk.sem.logic.Expression - This is the base abstract object for all logical expressionsnltk.sem.drt.DRS - A Discourse Representation Structure.nltk.sem.drt.DrtProposition - No class docstring; 0/2 instance variable, 2/11 methods documentednltk.sem.drt.PossibleAntecedents - No class docstring; 2/4 methods documentednltk.sem.logic.AbstractVariableExpression - This class represents a variable to be used as a predicate or entitynltk.sem.drt.DrtAbstractVariableExpression - No class docstring; 1/4 method documentednltk.sem.drt.DrtConstantExpression - Undocumentednltk.sem.drt.DrtFunctionVariableExpression - Undocumentednltk.sem.drt.DrtIndividualVariableExpression - Undocumentednltk.sem.drt.DrtEventVariableExpression - Undocumentednltk.sem.logic.ConstantExpression - This class represents variables that do not take the form of a single character followed by zero or more digits.nltk.sem.drt.DrtConstantExpression - Undocumentednltk.sem.logic.FunctionVariableExpression - This class represents variables that take the form of a single uppercase character followed by zero or more digits.nltk.sem.drt.DrtFunctionVariableExpression - Undocumentednltk.sem.logic.IndividualVariableExpression - This class represents variables that take the form of a single lowercase character (other than 'e') followed by zero or more digits.nltk.sem.drt.DrtIndividualVariableExpression - Undocumentednltk.sem.drt.DrtEventVariableExpression - Undocumentednltk.sem.logic.EventVariableExpression - This class represents variables that take the form of a single lowercase 'e' character followed by zero or more digits.nltk.sem.drt.DrtEventVariableExpression - Undocumentednltk.sem.logic.ApplicationExpression - This class is used to represent two related types of logical expressions.nltk.sem.drt.DrtApplicationExpression - No class docstring; 1/3 method documentednltk.sem.logic.BinaryExpression - No class docstring; 0/1 property, 0/2 instance variable, 2/7 methods documentednltk.sem.drt.DrtBinaryExpression - No class docstring; 1/3 method, 0/1 static method documentednltk.sem.drt.DrtBooleanExpression - Undocumentednltk.sem.drt.DrtConcatenation - DRS of the form '(DRS + DRS)'nltk.sem.drt.DrtOrExpression - Undocumentednltk.sem.drt.DrtEqualityExpression - Undocumentednltk.sem.logic.BooleanExpression - No class docstring; 1/1 method documentednltk.sem.drt.DrtBooleanExpression - Undocumentednltk.sem.drt.DrtConcatenation - DRS of the form '(DRS + DRS)'nltk.sem.drt.DrtOrExpression - Undocumentednltk.sem.logic.AndExpression - This class represents conjunctionsnltk.sem.logic.IffExpression - This class represents biconditionalsnltk.sem.logic.ImpExpression - This class represents implicationsnltk.sem.logic.OrExpression - This class represents disjunctionsnltk.sem.drt.DrtOrExpression - Undocumentednltk.sem.logic.EqualityExpression - This class represents equality expressions like "(x = y)".nltk.sem.drt.DrtEqualityExpression - Undocumentednltk.sem.logic.NegatedExpression - No class docstring; 0/1 property, 0/1 instance variable, 3/8 methods documentednltk.sem.drt.DrtNegatedExpression - No class docstring; 1/3 method documentednltk.sem.logic.VariableBinderExpression - This an abstract class for any Expression that binds a variable in an Expression. This includes LambdaExpressions and Quantified Expressionsnltk.sem.logic.LambdaExpression - No class docstring; 0/1 property, 1/2 method documentednltk.sem.drt.DrtLambdaExpression - No class docstring; 1/3 method documentednltk.sem.logic.QuantifiedExpression - No class docstring; 0/1 property, 1/2 method documentednltk.sem.logic.AllExpression - Undocumentednltk.sem.logic.ExistsExpression - Undocumentednltk.sem.logic.Tokens - Undocumentednltk.sem.drt.DrtTokens - Undocumentednltk.sem.logic.Type - Undocumentednltk.sem.logic.BasicType - Undocumentednltk.sem.logic.AnyType - Undocumentednltk.sem.logic.EntityType - Undocumentednltk.sem.logic.EventType - Undocumentednltk.sem.logic.TruthValueType - Undocumentednltk.sem.logic.ComplexType - Undocumentednltk.sem.logic.AnyType - Undocumentednltk.sem.logic.Variable - No class docstring; 0/1 instance variable, 1/8 method documentednltk.sentiment.sentiment_analyzer.SentimentAnalyzer - A Sentiment Analysis tool based on machine learning approaches.nltk.stem.wordnet.WordNetLemmatizer - WordNet Lemmatizernltk.tag.brill_trainer.BrillTaggerTrainer - A trainer for tbl taggers.nltk.tag.hmm.HiddenMarkovModelTrainer - Algorithms for learning HMM parameters from training data. These include both supervised learning (MLE) and unsupervised learning (Baum-Welch).nltk.text.ConcordanceIndex - An index that can be used to look up the offset locations at which a given word occurs in a document.nltk.text.ContextIndex - A bidirectional index between words and their 'contexts' in a text. The context of a word is usually defined to be the words that occur in a fixed window around the word; but other definitions may also be used by providing a custom context function.nltk.text.Text - A wrapper around a sequence of simple (string) tokens, which is intended to support initial exploration of texts (via the interactive console). Its methods perform a variety of analyses on the text's contexts (e...nltk.text.TextCollection - A collection of texts, which can be loaded with list of texts, or with a corpus consisting of one or more texts, and which supports counting, concordancing, collocation discovery, etc. Initialize a TextCollection as follows:...nltk.text.TokenSearcher - A class that makes it easier to use regular expressions to search over tokenized strings. The tokenized string is converted to a string where tokens are marked with angle brackets -- e.g., '<the><window><is><still><open>'...nltk.tokenize.punkt.PunktBaseClass - Includes common components of PunktTrainer and PunktSentenceTokenizer.nltk.tokenize.punkt.PunktSentenceTokenizer - A sentence tokenizer which uses an unsupervised algorithm to build a model for abbreviation words, collocations, and words that start sentences; and then uses that model to find sentence boundaries. This approach has been shown to work well for many European languages.nltk.tokenize.punkt.PunktTrainer - Learns parameters used in Punkt sentence boundary detection.nltk.tokenize.punkt.PunktLanguageVars - Stores variables, mostly regular expressions, which may be language-dependent for correct application of the algorithm. An extension of this class may modify its properties to suit a language other than English; an instance can then be passed as an argument to PunktSentenceTokenizer and PunktTrainer constructors.nltk.tokenize.punkt.PunktParameters - Stores data used to perform sentence boundary detection with Punkt.nltk.tokenize.punkt.PunktToken - Stores a token of text with annotations produced during sentence boundary detection.nltk.tokenize.texttiling.TokenSequence - A token list with its original length and its indexnltk.tokenize.texttiling.TokenTableField - A field in the token table holding parameters for each token, used later in the processnltk.tokenize.util.CJKChars - An object that enumerates the code points of the CJK characters as listed on http://en.wikipedia.org/wiki/Basic_Multilingual_Plane#Basic_Multilingual_Planenltk.toolbox.StandardFormat - Class for reading and processing standard format marker files and strings.nltk.toolbox.ToolboxData - No class docstring; 2/4 methods documentednltk.toolbox.ToolboxSettings - This class is the base class for settings files.nltk.translate.api.AlignedSent - Return an aligned sentence object, which encapsulates two sentences along with an Alignment between them.nltk.translate.api.PhraseTable - In-memory store of translations for a given phrase, and the log probability of the those translationsnltk.translate.gale_church.LanguageIndependent - Undocumentednltk.translate.ibm5.Slots - Represents positions in a target sentence. Used to keep track of which slot (position) is occupied.nltk.translate.ibm_model.AlignmentInfo - Helper data object for training IBM Models 3 and upnltk.translate.ibm_model.Counts - Data object to store counts of various parameters during trainingnltk.translate.ibm2.Model2Counts - Data object to store counts of various parameters during training. Includes counts for alignment.nltk.translate.ibm3.Model3Counts - Data object to store counts of various parameters during training. Includes counts for distortion.nltk.translate.ibm4.Model4Counts - Data object to store counts of various parameters during training. Includes counts for distortion.nltk.translate.ibm5.Model5Counts - Data object to store counts of various parameters during training. Includes counts for vacancies.nltk.translate.ibm_model.IBMModel - Abstract base class for all IBM modelsnltk.translate.stack_decoder._Hypothesis - Partial solution to a translation.nltk.translate.stack_decoder._Stack - Collection of _Hypothesis objectsnltk.translate.stack_decoder.StackDecoder - Phrase-based stack decoder for machine translationnltk.treeprettyprinter.TreePrettyPrinter - Pretty-print a tree in text format, either as ASCII or Unicode. The tree can be a normal tree, or discontinuous.nltk.twitter.twitterclient.Twitter - Wrapper class with restricted functionality and fewer options.nltk.twitter.util.Authenticate - Methods for authenticating with Twitter.strnltk.data.FileSystemPathPointer - A path pointer that identifies a file which can be accessed directly via a given absolute path.nltk.data.GzipFileSystemPathPointer - A subclass of FileSystemPathPointer that identifies a gzip-compressed file located at a given absolute path. GzipFileSystemPathPointer is appropriate for loading large gzip-compressed pickle objects efficiently.threading.Threadnltk.app.collocations_app.CollocationsModel.LoadCorpus - Undocumentednltk.app.concordance_app.ConcordanceSearchModel.LoadCorpus - Undocumentednltk.app.concordance_app.ConcordanceSearchModel.SearchCorpus - Undocumentednltk.downloader.DownloaderGUI._DownloadThread - Undocumentednltk.inference.api.TheoremToolThread - Undocumentedtkinter.Framenltk.draw.table.MultiListbox - A multi-column listbox, where the current selection applies to an entire row. Based on the MultiListbox Tkinter widget recipe from the Python Cookbook (http://code.activestate.com/recipes/52266/)tkinter.Menubuttonnltk.draw.util.MutableOptionMenu - No class docstring; 0/6 instance variable, 1/6 method documentedtuplenltk.featstruct.FeatureValueConcat - A base feature value that represents the concatenation of two or more FeatureValueTuple or Variable.nltk.featstruct.FeatureValueTuple - A base feature value that is a tuple of other base feature values. FeatureValueTuple implements SubstituteBindingsI, so it any variable substitutions will be propagated to the elements contained by the set...twython.Twythonnltk.twitter.twitterclient.Query - Retrieve data from the Twitter REST API.twython.TwythonStreamernltk.twitter.twitterclient.Streamer - Retrieve data from the Twitter Streaming API.typenltk.test.unit.lm.test_models.ParametrizedTests - Metaclass for generating parametrized tests.unittest.TestCasenltk.test.unit.lm.test_preprocessing.TestPreprocessing - Undocumentednltk.test.unit.lm.test_vocabulary.NgramModelVocabularyTests - tests Vocabulary Classnltk.test.unit.test_aline.TestAline - Test Aline algorithm for aligning phonetic sequencesnltk.test.unit.test_brill.TestBrill - Undocumentednltk.test.unit.test_cfd_mutation.TestEmptyCondFreq - Undocumentednltk.test.unit.test_cfg2chomsky.ChomskyNormalFormForCFGTest - Undocumentednltk.test.unit.test_chunk.TestChunkRule - No class docstring; 1/1 method documentednltk.test.unit.test_collocations.TestBigram - Undocumentednltk.test.unit.test_concordance.TestConcordance - Text constructed using: http://www.nltk.org/book/ch01.htmlnltk.test.unit.test_corenlp.TestParserAPI - Undocumentednltk.test.unit.test_corenlp.TestTaggerAPI - Undocumentednltk.test.unit.test_corenlp.TestTokenizerAPI - Undocumentednltk.test.unit.test_corpora.TestCess - Undocumentednltk.test.unit.test_corpora.TestCoNLL2007 - Undocumentednltk.test.unit.test_corpora.TestFloresta - Undocumentednltk.test.unit.test_corpora.TestIndian - Undocumentednltk.test.unit.test_corpora.TestMWAPPDB - Undocumentednltk.test.unit.test_corpora.TestPTB - Undocumentednltk.test.unit.test_corpora.TestSinicaTreebank - Undocumentednltk.test.unit.test_corpora.TestUdhr - Undocumentednltk.test.unit.test_corpus_views.TestCorpusViews - Undocumentednltk.test.unit.test_disagreement.TestDisagreement - Class containing unit tests for nltk.metrics.agreement.Disagreement.nltk.test.unit.test_freqdist.TestFreqDist - Undocumentednltk.test.unit.test_json2csv_corpus.TestJSON2CSV - No class docstring; 0/3 instance variable, 1/14 method documentednltk.test.unit.test_json_serialization.TestJSONSerialization - Undocumentednltk.test.unit.test_metrics.TestLikelihoodRatio - Undocumentednltk.test.unit.test_naivebayes.NaiveBayesClassifierTest - Undocumentednltk.test.unit.test_nombank.NombankDemo - Undocumentednltk.test.unit.test_pl196x.TestCorpusViews - Undocumentednltk.test.unit.test_pos_tag.TestPosTag - Undocumentednltk.test.unit.test_senna.TestSennaPipeline - Unittest for nltk.classify.sennanltk.test.unit.test_senna.TestSennaTagger - Unittest for nltk.tag.sennanltk.test.unit.test_stem.PorterTest - No class docstring; 3/7 methods documentednltk.test.unit.test_stem.SnowballTest - No class docstring; 1/5 method documentednltk.test.unit.test_tgrep.TestSequenceFunctions - Class containing unit tests for nltk.tgrep.nltk.test.unit.test_util.TestEverygrams - No class docstring; 0/1 instance variable, 1/6 method documentednltk.test.unit.test_wordnet.WordnNetDemo - Undocumentednltk.test.unit.translate.test_bleu.TestBLEU - No class docstring; 1/5 method documentednltk.test.unit.translate.test_bleu.TestBLEUFringeCases - Undocumentednltk.test.unit.translate.test_bleu.TestBLEUvsMteval13a - Undocumentednltk.test.unit.translate.test_bleu.TestBLEUWithBadSentence - Undocumentednltk.test.unit.translate.test_gdfa.TestGDFA - No class docstring; 1/1 method documentednltk.test.unit.translate.test_ibm1.TestIBMModel1 - Undocumentednltk.test.unit.translate.test_ibm2.TestIBMModel2 - Undocumentednltk.test.unit.translate.test_ibm3.TestIBMModel3 - Undocumentednltk.test.unit.translate.test_ibm4.TestIBMModel4 - Undocumentednltk.test.unit.translate.test_ibm5.TestIBMModel5 - Undocumentednltk.test.unit.translate.test_ibm_model.TestIBMModel - Undocumentednltk.test.unit.translate.test_meteor.TestMETEOR - Undocumentednltk.test.unit.translate.test_nist.TestNIST - Undocumentednltk.test.unit.translate.test_stack_decoder.TestHypothesis - Undocumentednltk.test.unit.translate.test_stack_decoder.TestStack - Undocumentednltk.test.unit.translate.test_stack_decoder.TestStackDecoder - UndocumentedValueErrornltk.internals.ReadError - Exception raised by read_* functions when they fail. :param position: The index in the input string where an error occurred. :param expected: What was expected when an error occurred.zipfile.ZipFilenltk.data.OpenOnDemandZipFile - A subclass of zipfile.ZipFile that closes its file pointer whenever it is not using it; and re-opens it when it needs to read data from the zipfile. This is useful for reducing the number of open file handles when many zip files are being accessed at once...