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finite-state toolkit, EM and Bayesian (Gibbs sampling) training for FST and context-free derivation forests
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carmel: finite state transducer toolkit (and k-best graph paths). http://www.isi.edu/licensed-sw/carmel/ EM and gibbs-sampled (pseudo-Bayesian) training (see carmel/README and carmel/carmel-tutorial). To build, cd carmel; make install (see carmel/LICENSE - free for research/non-commercial) forest-em: derivation forests EM and gibbs (dirichlet prior bayesian) training shared: generic routines used by both PREREQUISITES: Boost (http://boost.org - usually already installed in /usr) BUILDING: If this directory is not called 'graehl', then either rename it, or make sure there's a symlink from . to graehl (ln -s . graehl) in this directory. This is necessary for the headers <graehl/shared/X> to be located - or you can install the headers with ./install.sh /usr/local To specify where to install built binaries, do for e.g. Carmel: cd carmel INSTALL_PREFIX=/usr/local ARCH_FLAGS="-m32" make -j 4 install or, to build without installing (look in bin/`hostname`for binaries) make -j 4 You can have the binaries built in bin/YOURDIR by: make -j 4 BUILDSUB=YOURDIR If your system can't build static executables, you can: make -j 4 NOSTATIC=1 Static executables are already excluded if ARCH=macosx Requires GNU make 3.80 or later, and a recent C++ compiler (gcc 4.3 is known good, as is Microsoft Visual C++ 7.1) If you're mystified by what's going on in the build, look at shared/graehl.mk "make depend" should run automatically.
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finite-state toolkit, EM and Bayesian (Gibbs sampling) training for FST and context-free derivation forests
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