chenhaihua / legal-text-mining

Experiments classifying legal documents into their sub-categories: e.g. civil law, criminal law or administrative law. Classifiers used: k-Nearest Neighbours linear Support Vector Machine Random Forest Convolutional Neural Network Long Short Term Memory Neural Network

Legal Text Mining

Synopsis

Experiments classifying legal documents into their sub-categories: e.g. civil law, criminal law or administrative law. Classifiers used:

• k-Nearest Neighbours
• linear Support Vector Machine
• Random Forest
• Convolutional Neural Network
• Long Short Term Memory Neural Network

Usage

Convert raw XML (from rechtspraak.nl) to plain text using xmlToPlain.py

Make bag of words data file using to_bow_data.py

Make int vector model using to_vector_data.py

Bag of words classifiers can then be executed using bow_*.py, the LSTM using word2vec_lstm.py