Uses of Class
com.aliasi.corpus.Corpus

Packages that use Corpus

Package	Description
com.aliasi.classify	Classes for classifying data and evaluation.
com.aliasi.corpus	Classes for parsing and handling various corpora.
com.aliasi.crf	Classes and interfaces for conditional random fields.
com.aliasi.features	Classes for extracting feature vectors from objects and parsing objects for feature handlers.

Uses of Corpus in com.aliasi.classify

Methods in com.aliasi.classify with parameters of type Corpus

Modifier and Type	Method and Description
static Iterator<TradNaiveBayesClassifier>	TradNaiveBayesClassifier.emIterator(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories.
static Iterator<TradNaiveBayesClassifier>	TradNaiveBayesClassifier.emIterator(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories.
static TradNaiveBayesClassifier	TradNaiveBayesClassifier.emTrain(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount, int maxEpochs, double minImprovement, Reporter reporter) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories, maximum number of epochs, minimum improvement per epoch, and reporter to which progress reports are sent.
static TradNaiveBayesClassifier	TradNaiveBayesClassifier.emTrain(TradNaiveBayesClassifier initialClassifier, Factory<TradNaiveBayesClassifier> classifierFactory, Corpus<ObjectHandler<Classified<CharSequence>>> labeledData, Corpus<ObjectHandler<CharSequence>> unlabeledData, double minTokenCount, int maxEpochs, double minImprovement, Reporter reporter) Apply the expectation maximization (EM) algorithm to train a traditional naive Bayes classifier using the specified labeled and unabled data, initial classifier and factory for creating subsequent factories, maximum number of epochs, minimum improvement per epoch, and reporter to which progress reports are sent.
static <F> LogisticRegressionClassifier<F>	LogisticRegressionClassifier.train(Corpus<ObjectHandler<Classified<F>>> corpus, FeatureExtractor<? super F> featureExtractor, int minFeatureCount, boolean addInterceptFeature, RegressionPrior prior, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Returns a trained logistic regression classifier given the specified feature extractor, training corpus, model priors and search parameters.
static <F> LogisticRegressionClassifier<F>	LogisticRegressionClassifier.train(Corpus<ObjectHandler<Classified<F>>> corpus, FeatureExtractor<? super F> featureExtractor, int minFeatureCount, boolean addInterceptFeature, RegressionPrior prior, int blockSize, LogisticRegressionClassifier<F> hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegressionClassifier<F>> classifierHandler, Reporter reporter) Returns a trained logistic regression classifier given the specified feature extractor, training corpus, model priors and search parameters.

Constructors in com.aliasi.classify with parameters of type Corpus

Constructor and Description

PerceptronClassifier(Corpus<ObjectHandler<Classified<E>>> corpus, FeatureExtractor<? super E> featureExtractor, KernelFunction kernelFunction, String corpusAcceptCategory, int numIterations, String outputAcceptCategory, String outputRejectCategory) Construct a perceptron classifier from the specified feature extractor, corpus with designated accept category, polynomial kernel degree and number of training iterations, and output accept and reject categories.

Uses of Corpus in com.aliasi.corpus

Subclasses of Corpus in com.aliasi.corpus

Modifier and Type	Class and Description
class	DiskCorpus<H extends Handler> A DiskCorpus reads data from a specified training and test directory using a specified parser.
class	ListCorpus<E> A ListCorpus implements a corpus based on a list of training and test cases.
class	XValidatingObjectCorpus<E> An XValidatingObjectCorpus holds a list of items which it uses to provide training and testing items using cross-validation.

Uses of Corpus in com.aliasi.crf

Methods in com.aliasi.crf with parameters of type Corpus

Modifier and Type	Method and Description
static ChainCrfChunker	ChainCrfChunker.estimate(Corpus<ObjectHandler<Chunking>> chunkingCorpus, TagChunkCodec codec, TokenizerFactory tokenizerFactory, ChainCrfFeatureExtractor<String> featureExtractor, boolean addInterceptFeature, int minFeatureCount, boolean cacheFeatureVectors, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Return the chain CRF-based chunker estimated from the specified corpus, which is converted to a tagging corpus using the specified coder/decoder and tokenizer factory, then passed to the chain CRF estimate method along with the rest of the arguments.
static <F> ChainCrf<F>	ChainCrf.estimate(Corpus<ObjectHandler<Tagging<F>>> corpus, ChainCrfFeatureExtractor<F> featureExtractor, boolean addInterceptFeature, int minFeatureCount, boolean cacheFeatureVectors, boolean allowUnseenTransitions, RegressionPrior prior, int priorBlockSize, AnnealingSchedule annealingSchedule, double minImprovement, int minEpochs, int maxEpochs, Reporter reporter) Return the CRF estimated using stochastic gradient descent with the specified prior from the specified corpus of taggings of type F pruned to the specified minimum feature count, using the specified feature extractor, automatically adding an intercept feature if the flag is true, allow unseen tag transitions as specified, using the specified training parameters for annealing, measuring convergence, and reporting the incremental results to the specified reporter.

Uses of Corpus in com.aliasi.features

Constructors in com.aliasi.features with parameters of type Corpus

Constructor and Description
ZScoreFeatureExtractor(Corpus<ObjectHandler<Classified<E>>> corpus, FeatureExtractor<? super E> extractor) Construct a z-core feature extractor from the specified base feature extractor and the training section of the supplied corpus.