PyAdaGram is an online Adaptor Grammar model package, developed by the Cloud Computing Research Team in [University of Maryland, College Park] (http://www.umd.edu). You may find more details about this project on our papaer [Online Adaptor Grammars with Hybrid Inference] (http://kzhai.github.io/paper/2014_tacl.pdf) appeared in TACL 2014.
Please download the latest version from our GitHub repository.
Please send any bugs of problems to Ke Zhai (kzhai@umd.edu).
This package depends on many external python libraries, such as numpy, scipy and nltk.
Assume the PyAdaGram package is downloaded under directory $PROJECT_SPACE/src/, i.e.,
$PROJECT_SPACE/src/PyAdaGram
To prepare the example dataset,
tar zxvf brent-phone.tar.gz
To launch PyAdaGram, first redirect to the directory of PyAdaGram source code,
cd $PROJECT_SPACE/src/PyAdaGram
and run the following command on example dataset,
python -m launch_train --input_directory=./brent-phone/ --output_directory=./ --grammar_file=./brent-phone/grammar.unigram --number_of_documents=9790 --batch_size=10
The generic argument to run PyAdaGram is
python -m launch_train --input_directory=$INPUT_DIRECTORY/$CORPUS_NAME --output_directory=$OUTPUT_DIRECTORY --grammar_file=$GRAMMAR_FILE --number_of_documents=$NUMBER_OF_DOCUMENTS --batch_size=$BATCH_SIZE
You should be able to find the output at directory $OUTPUT_DIRECTORY/$CORPUS_NAME.
Under any circumstances, you may also get help information and usage hints by running the following command
python -m launch_train --help
To launch test script, run the following command
python -m launch_test --input_directory=$DATA_DIRECTORY --model_directory=$MODEL_DIRECTORY --non_terminal_symbol=$NON_TERMINAL_SYMBOL