MALLET中的话题模型

1.Training
 a
 Topic
Model

ParallelTopicModel lda = new ParallelTopicModel(8); lda.addInstances(instances); lda.estimate(); 

2.Evaluating 
a
 Topic
Model

MarginalProbEstimator evaluator = lda.getProbEstimator(); double logLikelihood = evaluator.evaluateLeftToRight(testing, 10, false, null); 

3.Inferring
 topics
 for
 new documents

TopicInferencer inferencer = lda.getInferencer(); double[] topicProbs = inferencer.getSampledDistribution(instance, 100, 10, 10); 

4.Dirichlet‐multinomial
 Regression

 

character:(1)Arbitrary 
observed 
features 
of 
documents(2)Target
contains
FeatureVector

DMRTopicModel dmr = new DMRTopicModel (numTopics); dmr.addInstances(training); dmr.estimate(); dmr.writeParameters(new File("dmr.parameters"));  

5.Polylingual
 Topics

InstanceList[] training =new InstanceList[] { english, german, arabic, mahican }; PolylingualTopicModel pltm = new PolylingualTopicModel(numTopics); pltm.addInstances(training);