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Jul 27, 2018

README.md

Few-Shot Charge Prediction with Discriminative Legal Attributes

Source code and datasets of COLING2018 paper: "Few-Shot Charge Prediction with Discriminative Legal Attributes". (pdf)

Dataset

Please download the dataset here, unzip it and you will get three folders: "data", "data_20w", "data_38w". Then put the folder "data" under this directory. It contains following files:

  • words.vec: Pre-trained word embeddings, each line contains a word and its embedding.
  • attributes: The legal attributes for each charge.
  • train: data for training from small dataset.
  • test: data for test from small dataset.
  • valid: data for validation from small dataset.

If you want to train and test on middle dataset, please copy the files in "data_20w" folder to "data" folder. If you want to train and test on large dataset, please copy the files in "data_38w" folder to "data" folder.

Run

Run the following command for training our model:

cd code/
python train.py

Dependencies

  • Tensorflow == 0.12
  • Scipy == 0.18.1
  • Numpy == 1.11.2
  • Python == 2.7

Log

After start training, a new folder "log" will be created.There are 4 directories in it:

  • /evaluation_charge_log/: stores model's performance of charge prediction on test data during training.
  • /evaluation_attr_log/: stores model's performance of attribute prediction on test data during training.
  • /validation_charge_log/: stores model's performance of charge prediction on validation data during training.
  • /validation_attr_log/: stores model's performance of attribute prediction on validation data during training.

Cite

If you use the code, please cite this paper:

Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, Maosong Sun. Few-Shot Charge Prediction with Discriminative Legal Attributes. The 27th Iinternational Conference on Computational Liguisitics (COLING 2018).

For more related works, please refer to my homepage.

About

The source code of our COLING'18 paper "Few-Shot Charge Prediction with Discriminative Legal Attributes".

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