Python project with the proposal's implementation from the paper:

A Hybrid Approach to Mining Conditions - HAIS, 2018 - Fernando O. Gallego and Rafael Corchuelo

• datasets/ Dataset's folder
  • dataset-en
  • dataset-en-lite
  • dataset-es
  • dataset-es-lite
• models/ Word2vec models' folder
  • w2v-modelv2-en
  • w2v-modelv2-en
• LICENCE
• candidates_creator.py
• main.py
• model_factory.py
• README
• validation.py
• word_preprocessing.py
• word_vectorizer.py

• Python 3.5.4 or above
• Theano 0.9.0
• Keras 2.0.8
• NLTK 3.2.4 with punkt and SnowballData models installed.
• Numpy 1.13.1
• Scikit-learn 0.19.0
• Gensim 2.3.0

main.py is the entry point of our experiments. It contains the following script parameters:

1. relative path of the dataset's file
2. language selected
3. relative path of the word2vec model's file
4. number of folds to perform k-fold cross validation
5. deep learning model to use in the experiment (name of the class inside model_factory.py)
6. relative path of the output csv file with the performance results
7. score threshold to consider whether a candidate is a condition or not

python main.py dataset/dataset-en en models/w2v-modelv2-en 4 ModelA results/results-ModelA-en.csv 0.75