From f8d9ff4aaeb1d1f773bacfe9ee75d1d1778ec26b Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Wed, 14 Nov 2012 20:33:51 -0500 Subject: major mert clean up, stuff for simple system demo --- compound-split/README | 51 --------------------------------------------------- 1 file changed, 51 deletions(-) delete mode 100644 compound-split/README (limited to 'compound-split/README') diff --git a/compound-split/README b/compound-split/README deleted file mode 100644 index b7491007..00000000 --- a/compound-split/README +++ /dev/null @@ -1,51 +0,0 @@ -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. - -- cgit v1.2.3