RestPose currently offers a very straightforward categorisation system, aimed mainly at performing language guessing (though it would be easy to plug more advanced algorithms into the system in future). The system requires a sample of text for each target category, and generates an ordered list of ngrams which are most significant in that sample.
The algorithm used is the same as that used by “libtextcat”, and based on a 1994 paper entitled “N-gram-based text categorization” by William B. Cavnar and John M. Trenkle. It is a very simple algorithm, but processes text extremely fast and performs the language guessing text extremely well (given suitable training data). At the time of writing, the paper can be downloaded from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.3248&rep=rep1&type=pdf
To train a model, you need some sample data. One approach for language guessing is to download some sample data from wikipedia, which can be done manually, or can be done using the script in scripts/get_wikipedia_sample_text.py. This script will download 512k of text from random wikipedia pages in each language supported by RestPose, and store it in a directory named lang_samples. This sample data can then be used to train the model.
Once you have sample data, the “restpose” program can be used on the command line to build a model, producing a JSON description of the model on stdout. This can be quite long, so you’ll probably want to redirect the output to a file. For example:
./restpose -a train -d lang_samples -l en -l fr
The above command will train a model using the sample data for english and french. To add each language, add a “-l lang” parameter, where lang is the language code for the language to add.
For convenience, the command to train a model using all the data downloaded by the scripts/get_wikipedia_sample_text.py script is:
./restpose -a train -d lang_samples -l da -l de -l en -l es -l fi -l fr -l hu -l it -l ja -l ko -l nl -l no -l pt -l ro -l ru -l sv -l tr -l zh