Here we implement the RST discourse parser described in A Two-stage Parsing Method for Text-level Discourse Analysis.
The best-trained models are put in the
data/model folder. Due to the licence of RST data corpus, we can't include the data in our project folder. To reproduce the result in the paper, you need to download it from the LDC, preprocess the data as we state below and evaluate the model with
python3 main.py --eval --eval_dir EVAL_DIR.
Preprocess the data:
This project relies on Stanford CoreNLP toolkit to preprocess the data. You can download from here and put the file run_corenlp.sh into the CoreNLP folder. Then use the following command to preprocess both the data in train_dir and in test_dir.
python3 preprocess.py [-h] --data_dir DATA_DIR --corenlp_dir CORENLP_DIR
Extract feature templates, action maps and relation maps:
python3 main.py --prepare --train_dir TRAIN_DIR --eval_dir EVAL_DIR
python3 main.py --train --train_dir TRAIN_DIR
python3 main.py --eval --eval_dir EVAL_DIR
All the codes are tested under Python 3.5. And see requirements.txt for python library dependency and you can install them with pip.