Carmel finite-state toolkit - J. Graehl
(carmel includes EM and gibbs-sampled (pseudo-Bayesian) training)
(see carmel/LICENSE - free for research/non-commercial)
(see carmel/README and carmel/carmel-tutorial).
Building from source
cd carmel; make -j 4 install BOOST_SUFFIX=-mt INSTALL_PREFIX=/usr/local
# BOOST_SUFFIX= depends on how your boost libraries are installed - ls /usr/lib/libboost*.so
(prerequisites: GNU Make (3.8) C++ compiler (GCC 5, clang 3.7, or visual studio 2015 will do) and Boost, which you probably already have on your linux system; for Mac, you can get them from Homebrew. For windows: MSVC2015 should work; you can also use cygwin or mingw.
make options
If your system doesn't support static linking, make NOSTATIC=1
If you're trying to modify or troubleshoot the build, take a look at
graehl/shared/graehl.mk as well as carmel/Makefile; you shouldn't need to
manually run make depend.
Subdirectories
-
carmel: finite state transducer toolkit with EM and gibbs-sampled (pseudo-Bayesian) training -
forest-em: derivation forests EM and gibbs (dirichlet prior bayesian) training -
graehl/shared: utility C++/Make libraries used by carmel and forest-em -
gextract: some python bayesian syntax MT rule inference -
sblm: some simple pcfg (e.g. penn treebank parses, but preferably binarized) -
clm: some class-based LM feature? I forget. -
cipher: some word-class discovery and unsupervised decoding of simple probabilistic substitution cipher (uses carmel, but look to the tutorial in carmel/ first) -
util: misc shell/perl scripts