LingPipe | Forum profile

syam_mp wrote: > Hi All, > I am new user wanting to use LingPipe for prototyping a tool which will help me > 1. Create a topic list from existing text data. > I suggest you do Latent Dirichlet allocation (LDA) as per the tutorial in: http://alias-i.com/lingpipe/demos/tutorial/cluster /read-me.html Topic summaries/labels can be suggested by the highest ranked words in the identified...

There are other subtle differences, but the main difference is that tokenizers aren't quite as heavy or as general as chunkers. Chunkers are heavier because they return Chunking objects, which contain sets of Chunk objects, which contain starts, ends, types, and scores. You can't get the string spanned by a chunk back from the chunk itself -- you need the char sequence underlying the ...

Brian Frutchey wrote: > I am hoping to improve the performance of my LingPipe Named Entity Recognition tests. Currently I am only able to extract entities from about 5K of text/sec using either of the below methods: > > {code} > Chunker chunker = > (Chunker)AbstractExternalizable.readObject(new > File("ne-en-news-muc6.AbstractCharLmRescoringChunk er")); > Chunking entities = chunker.chunk...

I'm afraid to say there is no "running LingPipe". LingPipe is just a set of Java APIs. That means you need to write Java code to access LingPipe's functionality. (In this way, it's just like Lucene.) The commands (.bat and .sh files) are just there for demo purposes. You can look at the code for them in $LINGPIPE/demos/generic and design your own commands or embed the same processing ...

thanks for the quick response. your results make much more sense. May be I am doing something wrong with the lingpipe package and thats why found it difficult to interpret the lingpipe output : And my U,V matrices are : >> U: 0 1 0 -0.69 0.65 1 0.0539 -0.205 2 -0.721 0.728 >> V: 0 1 0 -0.74 -0.141 1 0.35 -0.613 2 -0.494 0.776 3 0.271 0.0207 -Prasen On Sun, Oct 18, 2009 ...

BA YORO I can probably help with the Wikipedia processing, as I have custom and existing perl modules for that. I have converted wikipedia to dictd format before. If you have server space I can set it up on that, as my own computer systems are overstretched. I'm interested in using Wikipedia (and more) to build a very large Word/Phrase/Acronym definition system, using word sense induction ...

The SVD algorithm should work for any matrix. There are two variants -- sparse and partial. If your matrix has specified values, and the others are zeroes, use the svd() method. If the matrix has some known values and the others are unknown, use the partialSvd() method. You might have a learning rate that's too high for the size/density of the problem. Have you tried different (...

prasenjit mukherjee wrote: > Is there a utility class to generate a random matrix ( given > dimensions m,n ) in lingpipe ? No, but it's really easy. If you want to populate an M x N matrix with a random double between 0 and 1: int M = 5; // rows int N = 7; // columns Random random = new Random(); Matrix m = new DenseMatrix(M,N); for (int m = 0; m < M; ++m) ____for (int n = 0; n < N;...

syam_mp wrote: > Hi All, > I am new user wanting to use LingPipe for prototyping a tool which will help me > 1. Create a topic list from existing text data. > I suggest you do Latent Dirichlet allocation (LDA) as per the tutorial in: http://alias-i.com/lingpipe/demos/tutorial/cluster /read-me.html Topic summaries/labels can be suggested by the highest ranked words in the identified...