@chapter Bag Of Words Library README@c set the vars BOW_VERSION@include version.texi@samp{libbow}, version @value{BOWVERSION}.@include libbow-desc.texi@section Rainbow@samp{Rainbow} is a standalone program that does documentclassification.  Here are some examples:@itemize @bullet@item@examplerainbow -i ./training/positive ./training/negative@end exampleUsing the text files found under the directories@file{./positive} and @file{./negative},tokenize, build word vectors, and write the resulting data structuresto disk.@item@examplerainbow -q ./testing/254@end exampleTokenize the text document @file{./testing/254}, and classify it,producing output like:@example/home/mccallum/training/positive 0.72/home/mccallum/training/negative 0.28@end example@item@examplerainbow -t 5@end examplePerform 5 trials, each consisting of a test/train split, a resetting ofweights according to the new split, and outputs of the classification ofthe test documents.@end itemizeTyping @samp{rainbow --help} will give list of all rainbow options.After you have compiled @samp{libbow} and @samp{rainbow}, you can runthe shell script @file{./demo/script} to see an annotated demonstrationof the classifier in action.The web pagehttp://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/naive-bayes.htmlhas a pointer to a ``Gentle Introduction to Rainbow'', as well as somesample UseNet text data.@formatRainbow improvements coming soon:   Better documentation.   Better modularily of command-line options for changing parameters     of weight-setting methods.   Incremental model training.   Better smoothing.  Good-Turing estimates, etc.@end format@section Arrow@samp{Arrow} is a standalone program that does document retrieval.Sorry, there is no documentation yet.