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Turbo topics find significant multiword phrases in topics.
This project forked from blei-lab/turbotopics
Turbo topics find significant multiword phrases in topics.
------------------- --- TURBOTOPICS --- ------------------- --------------------------------------------------------------------------- (C) Copyright 2009, David M. Blei ([email protected]) This file is part of TURBOTOPICS. TURBOTOPICS is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. TURBOTOPICS is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA --------------------------------------------------------------------------- This code contains python scripts for running the TURBOTOPICS method on either a corpus of documents or a corpus of documents combined with the output of LDA-C. for both scripts, the corpus is a file of the original text of documents, one per line. Note that neither script requires specifying how large N should be in the N-grams. For more information about the method, see the paper at http://arxiv.org/abs/0907.1013 The two scripts are compute_ngrams.py: Compute recursive multi-word expressions from a corpus. This will write out a file of vocabulary (including multi-word expressions) and their counts. lda_topics.py: Compute multi-word expressions per-topic from a corpus and LDA-C fit. (Note: the argument --ntopics is the same as K in LDA-C.) This will write out a file for each topic with the expressions and counts. Again, see the paper for details. Any questions/comments about this code should be posted to the topic models mailing list. Subscribe at https://lists.cs.princeton.edu/mailman/listinfo/topic-models
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