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author | Avneesh Saluja <asaluja@gmail.com> | 2013-03-28 18:28:16 -0700 |
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committer | Avneesh Saluja <asaluja@gmail.com> | 2013-03-28 18:28:16 -0700 |
commit | 3d8d656fa7911524e0e6885647173474524e0784 (patch) | |
tree | 81b1ee2fcb67980376d03f0aa48e42e53abff222 /compound-split/README.md | |
parent | be7f57fdd484e063775d7abf083b9fa4c403b610 (diff) | |
parent | 96fedabebafe7a38a6d5928be8fff767e411d705 (diff) |
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diff --git a/compound-split/README.md b/compound-split/README.md new file mode 100644 index 00000000..b7491007 --- /dev/null +++ b/compound-split/README.md @@ -0,0 +1,51 @@ +Instructions for running the compound splitter, which is a reimplementation +and extension (more features, larger non-word list) of the model described in + + C. Dyer. (2009) Using a maximum entropy model to build segmentation + lattices for MT. In Proceedings of NAACL HLT 2009, + Boulder, Colorado, June 2009 + +If you use this software, please cite this paper. + + +GENERATING 1-BEST SEGMENTATIONS AND LATTICES +------------------------------------------------------------------------------ + +Here are some sample invokations: + + ./compound-split.pl --output 1best < infile.txt > out.1best.txt + Segment infile.txt according to the 1-best segmentation file. + + ./compound-split.pl --output plf < infile.txt > out.plf + + ./compound-split.pl --output plf --beam 3.5 < infile.txt > out.plf + This generates denser lattices than usual (the default beam threshold + is 2.2, higher numbers do less pruning) + + +MODEL TRAINING (only for the adventuresome) +------------------------------------------------------------------------------ + +I've included some training data for training a German language lattice +segmentation model, and if you want to explore, you can or change the data. +If you're especially adventuresome, you can add features to cdec (the current +feature functions are found in ff_csplit.cc). The training/references are +in the file: + + dev.in-ref + +The format is the unsegmented form on the right and the reference lattice on +the left, separated by a triple pipe ( ||| ). Note that the segmentation +model inserts a # as the first word, so your segmentation references must +include this. + +To retrain the model (using MAP estimation of a conditional model), do the +following: + + cd de + ./TRAIN + +Note, the optimization objective is supposed to be non-convex, but i haven't +found much of an effect of where I initialize things. But I haven't looked +very hard- this might be something to explore. + |