What is this thing? See http://www.isi.edu/licensed-sw/carmel/carmel-tutorial2.pdf
(carmel includes EM and gibbs-sampled (pseudo-Bayesian) training)
(see carmel/LICENSE - free for research/non-commercial)
(see carmel/README and carmel/carmel-tutorial).
Recommended: cmake
cmake -B build . -G "Unix Makefiles" && cmake --build build
# recommend -G Ninja instead
from Makefile (some LDFLAGS CFLAGS customization may be needed to find libs)
brew install boost@1.79 # other version are fine too cd carmel; make -j 4 carmel BOOST_SUFFIX=-mt
sudo yum install boost169-devel cd carmel; make -j 4 carmel BOOST_INCLUDEDIR=/usr/include/boost169 LDFLAGS+=" -L/usr/lib64/boost169" BOOST_SUFFIX:=
(prerequisites: GNU Make (3.8) C++17 capable compiler (GCC 8, or
visual studio 2017 will do) and [Boost](http://boost.org), which you
probably already have on your linux system; for Mac, you can get them
from [Homebrew](http://brew.sh/). Native windows builds 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