All Classes and Interfaces
Class
Description
A base
ObjectStream implementation.An interface for generating features for name entity identification and for
updating document level contexts.
The result of a normalization that keeps the original text alongside the normalized form and a
full
Alignment between them.A bidirectional alignment between an original text and a normalized form of it.
Builds an
Alignment as the normalized text is produced, by recording each edit in order.One annotation of a
Document: a typed value anchored to a Span of the
document's original text, or a span-less value under a
document-scoped key.Parser for command line arguments.
Utility class for simple vector arithmetic.
Provides access to model persisted artifacts.
Responsible to create an artifact from an
InputStream.Represents a minimal tuple of information.
Common format parameters.
Common training parameters.
Interface for context generators used with a sequence beam search.
Provides fixed size, pre-allocated, least recently used replacement cache.
A configurable class of Unicode code points and the cursor based operations over it.
A char sequence normalizer, used to adjusting (prune, substitute, add, etc.)
characters in order to remove noise from text
The interface for chunkers which provide chunk tags for a sequence of tokens.
Interface for a
BeamSearchContextGenerator used in syntactic chunking.A marker interface for evaluating
chunkers.Class for holding chunks for a single unit of text.
Encapsulates a classpath entry that is associated with a
model URI
and optional properties.Describes a scanner which detects OpenNLP specific model files in an applications's classpath.
An immutable set of Unicode code points with O(1) membership.
Holds constituents when reading
parses.Class which associates a real valued parameter or expected value with a particular contextual
predicate or feature.
Common cross validator parameters.
Represents an indexer which compresses events in memory and performs feature selection.
Describes generic ways to read data from a
DataInputStream.A
Detokenizer merges tokens back to their detokenized representation.This enum contains an operation for every token to merge the
tokens together to their detokenized form.
A marker interface for evaluating
doccat.An offset-anchored annotation container: the original text of one document plus any
number of typed annotation layers over it.
How
Document.merge(Document, DuplicateLayerPolicy) treats a layer key that is
present on both documents.Runs a fixed sequence of
annotators over a text, producing
one Document that carries every step's layers.Assembles a
DocumentAnalyzer from annotators in execution order.A pipeline step that reads layers from a
Document and returns a new document
with its own layers added.Support methods shared by
DocumentAnnotator implementations: the
required-layer check and the per-sentence walk over the token layer.Receives one sentence's contiguous token run during
DocumentAnnotators.forEachSentence(List, List, SentenceTokenConsumer).Interface for classes which categorize documents.
Interface for processing an entire document allowing a
TokenNameFinder to use context
from the entire document.Class which holds a classified document and its category.
Encoding parameter.
EntityLinkers establish connections with external data to enrich extracted
entities.
Properties wrapper for
EntityLinker implementations.Common evaluation parameters.
The context of a decision point during training.
A specialized
Trainer that is based on a 'EventModelSequence' approach.Indicates that a certain API feature is not stable
and might change with a new release.
The
ExtensionLoader is responsible to load extensions to the OpenNLP library.Exception indicates that an OpenNLP extension could not be loaded.
Interface for generating features for document categorization.
The
FeatureGeneratorResourceProvider provides access to the resources
available in the model.Common evaluation parameters.
The
FMeasure is a utility class for evaluators
which measures precision, recall and the resulting f-measure.Represents a labeler for nodes which contain traces so that these traces can be predicted
by a
Parser.Encoder for head rules associated with parsing.
Allows repeated reads through a stream for certain model building types.
This exception indicates that the provided training data is
insufficient to train a desired model.
Classes, fields, or methods annotated
@Internal are for OpenNLP
internal use only.This exception indicates that a resource violates the expected data format.
Class for holding the document language and its confidence
Validates language codes against ISO 639 standards.
The interface for
LanguageDetector which predicts the Language for a context.A context generator interface for
LanguageDetector.A marker interface for evaluating
language detectors.A language model can calculate the probability p (between 0 and 1) of a
certain sequence of tokens, given its underlying vocabulary.
Holds a classified document and its
Language.Dictionary-driven segmentation for languages written without spaces: a Viterbi
search over the word lattice of a
MecabDictionary, minimizing the sum of
word costs and connection costs.Identifies one annotation layer of a
Document and carries the type of that
layer's annotation values, so reading a layer back is statically typed.How the annotations of a layer relate to the document text.
The standard
layer keys for the results the toolkit produces itself.Represents a lemmatized sentence.
The common interface for lemmatizers.
Interface for the context generator used for probabilistic
Lemmatizer.A marker interface for evaluating
lemmatizers.Interface for maximum entropy models.
Calculates the arithmetic mean of values
added with the
Mean.add(double) method.An immutable, in-memory dictionary in the
MeCab directory format: lexicon entries
from the
*.csv files, connection costs from matrix.def, character
categories from char.def, and unknown-word templates from unk.def,
loaded from a user-supplied dictionary directory.A model type to pattern enumeration.
Enumeration of supported model types.
One morpheme from lattice segmentation: the
Span it covers in the original
text, its surface form, and the feature columns its dictionary entry carries.Interface that allows
TagDictionary entries to be added and removed.Interface for generating the context for a
name finder by
specifying a set of feature generators.Encapsulates names for a single unit of text.
Reads
objects from a stream.A
CharSequenceNormalizer that can additionally report the Alignment from its
normalized output back to the input, so a span found in the normalized text maps to the exact
character offsets of the original.A
TokenNameFinder that can additionally report detected spans in the character coordinates
of the original input, mapping back through any text normalization applied before detection.Data structure for holding parse constituents.
Defines common methods for full-syntactic parsers.
A marker interface for evaluating
parsers.Enumeration of event types for a
Parser.Enumeration of supported
Parser types.Reads a plain text file and returns each line as a
String object.Interface for a
BeamSearchContextGenerator used in POS tagging.Represents an pos-tagged
sentence.The interface for part of speech taggers.
A marker interface for evaluating
pos taggers.This interface allows one to implement a prior distribution for use in
maximum entropy model training.
A marker interface for classes with probabilistic capabilities.
This interface makes an
Iterator resettable.Shared upper bounds for counts read from user-supplied resources, so a crafted
file cannot force an outsized allocation before validation completes.
An iterator for a list which returns values in the opposite order as the typical list iterator.
Represents a generic type of processable elements.
Interface for
SentenceDetector context generators.The interface for sentence detectors, which find the sentence boundaries in
a text.
A
SentenceSample contains a document with
begin indexes of the individual sentences.An sentiment specific
EvaluationMonitor to be used by the evaluator.Class for holding text used for sentiment analysis.
Class which models a sequence.
Represents a weighted sequence of outcomes.
A classification model that can label an input
Sequence.A codec for sequences of type
T.Interface for streams of
sequences used to train sequence models.A marker interface so that implementing classes can refer to
the corresponding
ArtifactSerializer implementation.Class for storing start and end integer offsets.
Reduces a word to its root form.
A factory that captures a stemmer configuration and mints configured
Stemmer
instances on demand.Stop criteria for model training.
A pluggable filter that decides whether a token (or a sequence of tokens)
is a stopword that should be removed during downstream text processing.
A marker-interface for a String interner implementation.
One subword unit produced by a
SubwordTokenizer, including the model representation
and source range.Splits text into subword units from a fixed model vocabulary, reporting the model id and
original-text span for each unit.
Interface to determine which tags are valid for a particular word
based on a tag dictionary.
Exception to terminate the execution of a command line tool.
One entry of a term vector layer: a term, how often it occurs in the document, and
where.
Classes, fields, or methods annotated
@ThreadSafe are safe to use
in multithreading contexts.Interface for context generators required for
tokenizer implementations.The interface for tokenizers, which segment a string into its tokens.
A marker interface for evaluating
tokenizers.The interface for name finders which provide name tags for a sequence of tokens.
A marker interface for evaluating
name finders.A
TokenSample is text with token spans.Represents a common base for training implementations.
Configuration used for model training.
Enumeration of Training measures.
An interface to capture training progress of a model.
Common training parameters.
Reference data for Unicode dashes, plus O(1) membership lookups.
The Unicode general category of a dash code point.
One Unicode dash code point and its reference attributes.
Reference data for Unicode whitespace, plus O(1) membership lookups.
Line-breaking behavior, mirroring the "Notes" column of the reference table.
Unicode general category for a whitespace or related code point.
One related code point that is commonly confused with whitespace but is not
(
White_Space=no).One Unicode whitespace code point and its reference attributes.
Frequency-driven segmentation for Chinese and similar scripts: a Viterbi search that
maximizes the summed log-probability of the words in a user-supplied frequency
lexicon, with unlisted characters falling back to single-character words.
Selects the whitespace definition used by
StringUtil.isWhitespace(char) and
StringUtil.isWhitespace(int): the Unicode White_Space property, or
OpenNLP's legacy definition from 1.x/2.x.A basic
Tokenizer implementation which performs tokenization
using white spaces.A
SubwordTokenizer implementing the BERT tokenization stages: basic tokenization
(control removal, whitespace normalization, CJK isolation, optional lower casing with accent
stripping, punctuation isolation) followed by greedy longest-match wordpiece segmentation.A
Tokenizer implementation which performs tokenization
using word pieces.A word vector.
A table that maps tokens to word vectors.