latent-semantic-indexing

Is there any open source implementation of LSI in Java? I want to use that library for my project. I have seen jLSI but it implements some other model of LSI. I want a standard model. Have you considered LDA (Latent Dirichlet allocation)? I haven't really either, but I encountered the same problem with LSI recently (patents). From what I understand LDA is a related/more powerful technique. http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation apparently has some links to open-source implementations. A google search for java LSI leads to a similar question that recommends SemanticVectors. A

I am working on a project which requires me to match a phrase or keyword with a set of similar keywords. I need to perform semantic analysis for the same. an example: Relevant QT cheap health insurance affordable health insurance low cost medical insurance health plan for less inexpensive health coverage Common Meaning low cost health insurance Here the the word under Common Meaning column should match the under Relevant QT column. I looked at a bunch of tools and techniques to do the same. S-Match seemed very promising, but I have to work in Python, not in Java. Also Latent Semantic Analysis

Is it possible to do clustering in gensim for a given set of inputs using LDA? How can I go about it? LDA produces a lower dimensional representation of the documents in a corpus. To this low-d representation you could apply a clustering algorithm, e.g. k-means. Since each axis corresponds to a topic, a simpler approach would be assigning each document to the topic onto which its projection is largest. Yes you can. Here is a tutorial: http://nlp.fi.muni.cz/projekty/gensim/wiki.html#latent-dirichlet-allocation First load you corpus, then call: lda = gensim.models.ldamodel.LdaModel(corpus=mm,

I have been working on latent semantic analysis lately. I have implemented it in java by making use of the Jama package. Here is the code: Matrix vtranspose ; a = new Matrix(termdoc); termdoc = a.getArray(); a = a.transpose() ; SingularValueDecomposition sv =new SingularValueDecomposition(a) ; u = sv.getU(); v = sv.getV(); s = sv.getS(); vtranspose = v.transpose() ; // we obtain this as a result of svd uarray = u.getArray(); sarray = s.getArray(); varray = vtranspose.getArray(); if(semantics.maketerms.nodoc>50) { sarray_mod = new double[50][50]; uarray_mod = new double[uarray.length][50];