Search Results for "lda"

The Java package jLDADMM is released to provide alternative choices for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. See the usage of jLDADMM in its website at http://jldadmm.sourceforge.net/

The TextProcessor Java package is a text processing toolkit, which provides some frequently used text processing functions such as stemming, removing stop-words, generating a term vocabulary, and calculating the term-doc frequency matrix. Basic topic mining models such as LDA and sparse NMF are also supported. The package can also generate feature files from a given text dataset with LDA and LIBSVM format for posterior procedures such as classification or clustering. The toolkit is also being extended for more advanced text analysis tasks based on natural language processing techniques.

.... --- Highlights: --- org.knowceans.util: IndexQuickSort, TableList: apply order of one array/list to others +++ Vectors, ArrayUtils: array convenience +++ RandomSamplers, CokusRandom, ArmSampler, Densities: random sampling and distributions +++ Arguments: command line parser +++ StopWatch, Which, ExternalProcess: runtime stuff +++ ParallelFor: OpenMP workalike +++ PatternString, NamedGroupRegex: regex convenience --- org.knowceans.corpus: CorpusSearcher: full-text search engine +++ LabelNumCorpus: svmlight corpus storage and filtering +++ NIPS corpus with text, authors, labels and citations --- org.knowceans.map: InvertibleHashMultiMap, BijectiveHashMap: implement n:m and 1:1 relations. --- Other libs: knowceans-arms = port of the Adaptive Rejection Metropolis Sampler (ARMS) for arbitrary distributions +++ lda-j = port of lda-c, implementing Latent Dirichlet Allocation (LDA)

A project aims to develop a system which trains LDA model in distributed enviorenment. I studied Hadoop based solution and found that Hadoop is not fit for distributed LDA training case. In this project I implement a platform based on socket.

A Java Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for Parameter Estimation and Inference