<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 TRANSITIONAL//EN"> <HTML> <HEAD> <META HTTP-EQUIV="Content-Type" CONTENT="text/html; CHARSET=UTF-8"> <META NAME="GENERATOR" CONTENT="GtkHTML/3.10.3"> </HEAD> <BODY> On Thu, 2006-10-19 at 17:53 +0200, Stephan Oepen wrote: <BLOCKQUOTE TYPE=CITE> <PRE> hei! > I would like to train and evaluate our current German treebank. The > treebank is, however, distributed across different profiles. Is there > a way to select more than one profile as the source for parameter > estimation? Or do I have to combine the profiles into one large > profile? well, due to popular request, here is my secret about virtual profiles: as of late, it is possible to create `virtual profiles', which can then serve as the target profile for _some_ [incr tsdb()] operations. a virtual profile, like any other profile, is a directory somewhere in the [incr tsdb()] profile database `home' directory. the only file one needs to put into a virtual profile directory is one called `virtual'. the virtual file, in turn, contains the profile names of sub-profiles, e.g. "jh0" "jh1" "jh2" "jh3" "jh4" "jh5" "ps" "tg" here, `jh0' et al. must be valid profile names (visible in the podium), and the double quotes are mandatory. a few restrictions: virtual profiles are read-only and currently do not show in the [incr tsdb()] podium. yet, they can be useful in training and evaluating parse selection models. berthold, if you got the latest LOGON build (or CVS), that now includes a sub-directory `lingo/redwoods/', which provides the current versions of the LOGON ERG treebanks (called JHPSTG). also, there is a script by the name `load' (essentially setting up the environment for a variety of experimental tasks) and input files `fc.lisp' (creating the feature cache, a one-time operation); `grid.lisp' (executing a large number of experiments, with varying feature sets and estimation parameters); </PRE> </BLOCKQUOTE> Hi Stephan, it is really a large number of experiments. I started my first experiment about a week ago, and I am not even into grandparenting yet. Is there a way to speed things up, e.g. by dropping some less interesting variation in parameters? Or is there any support for multiprocessing? Some parameters are not really self-explanatory. Can you provide some comments on grid.lisp? Which parameters are now supported in Pet? Thanks, Berthold <BLOCKQUOTE TYPE=CITE> <PRE> and finally `train.lisp' (training and serializing a model, using a default set of parameters). you should be able to adapt all of this for your Eiche treebank data. note that, since virtual profiles are read-only, you will still need a skeleton for the full data set, as each iteration in `grid.lisp' needs to write scores et al. generally, i would suggest to always use the LOGON tree for parse selection experiments. it also includes suitable TADM (and SVM) binaries. emily and francis, i hope you might find this useful too :-). best - oe +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +++ Universitetet i Oslo (IFI); Boks 1080 Blindern; 0316 Oslo; (+47) 2284 0125 +++ CSLI Stanford; Ventura Hall; Stanford, CA 94305; (+1 650) 723 0515 +++ --- <A HREF="mailto:oe@csli.stanford.edu">oe@csli.stanford.edu</A>; <A HREF="mailto:oe@ifi.uio.no">oe@ifi.uio.no</A>; <A HREF="mailto:stephan@oepen.net">stephan@oepen.net</A> --- +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ </PRE> </BLOCKQUOTE> </BODY> </HTML>