Awesome
law-turk
Predicting Decisions of Turkish Higher Courts.
Code to reproduce results given in "Natural Language Processing in Law: Prediction of outcomes in the Higher Courts of Turkey" by Emre Mumcuoğlu, Ceyhun E. Öztürk, Haldun M. Ozaktas and Aykut Koç (https://www.sciencedirect.com/science/article/abs/pii/S0306457321001692, https://doi.org/10.1016/j.ipm.2021.102684).
Requirements
- scikit-learn==0.24.2
- tensorflow==2.3.0 or tensorflow==2.8.0
- gensim==3.8.3
Tested versions of the required packages are given above. The code was tested in Python 3.8.
The deep learning models require the use of word embeddings. Download a Turkish word embedding model into data. You can use the one we used at https://github.com/akoksal/Turkish-Word2Vec
Use
Simply call predict.py with appropriate arguments.
- Court name: Should be one of constitutional, civil, criminal, administrative, taxation, constitutional_right1, constitutional_right2, constitutional_right3, constitutional_right4, constitutional_right5, constitutional_right6, constitutional_right7.
- Model name: Should be one of Dummy, DT, RF, SVM, GRU, LSTM, BiLSTM.
- Mode: Either training or test. Use test mode to print test results after training.
- Optional argument --attention: Whether to use attention mechanism in deep learning models.
An example call:
python3 predict.py constitutional BiLSTM training --attention