-
Notifications
You must be signed in to change notification settings - Fork 9
/
readme.txt
57 lines (38 loc) · 2.01 KB
/
readme.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
-------------------
--- 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