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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

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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

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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

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  • Python 100.0%