Experiments in the article (the/a(n)/0) generation task on WSJ data, conducted as part of my master thesis at the Faculty of Mathematics and Physics, Charles University in Prague (2017).
- MXPOST tagger http://www.inf.ed.ac.uk/resources/nlp/local_doc/MXPOST.html
- Collins parser http://www.cs.columbia.edu/~mcollins/code.html
- Stanford parser https://nlp.stanford.edu/software/lex-parser.shtml
- Penn Treebank https://catalog.ldc.upenn.edu/ldc99t42
- BNC http://www.natcorp.ox.ac.uk/
- 1-billion-word-benchmark http://www.statmt.org/lm-benchmark/
- KenLM language model https://kheafield.com/code/kenlm/
- XGBoost https://github.com/dmlc/xgboost
the corresponding paths need to be set in the .settings file
Follow the targets in the corresponding ./data_preparation/Makefile to extract, tag and parse sentences from the WSJ part of the Penn Treebank.
To prepare data for the countability feature, follow the targets in ./data_preparation/bnc/Makefile
To prepare data for the language model feature, execute the preprocess-billion-benchmark target in ./data_preparation/Makefile
To extract the features used by the classifiers, first prepare decision lists for the countability feature: cd extract_features/countability_bnc/ && make countability-bnc. Then train a language model on the 1-billion-word benchmark: cd experiments && make kenlm-train-ggl-5-with-nbs-cls3. Then, to extract all the features for the wsj corpus run extract-penn-features target in extract_features/Makefile. Finally, to prepare the extracted features for machine learning algorithms, postprocess them by the postprocess-features target in experiments/Makefile.
To train and evaluate logistic regression models: cd experiments && make lr-train-from-file. The model expects an instruction file specifying the parameters of the model, such as the regularization parameter and the features to use (logs/experiments/model_results/penn/instructions.csv), for the format of the file, see lib.train_model_on_postprocessed_features.train_model.
For experimenting with the gradient boosted tree models, follow the jupyter notebook in experiments/notebook_lee_tuning/XGBoost.ipynb