https://github.com/thunlp/attribute_charge
The source code of our COLING'18 paper "Few-Shot Charge Prediction with Discriminative Legal Attributes".
https://github.com/thunlp/attribute_charge
legal-ai
Last synced: 24 days ago
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The source code of our COLING'18 paper "Few-Shot Charge Prediction with Discriminative Legal Attributes".
- Host: GitHub
- URL: https://github.com/thunlp/attribute_charge
- Owner: thunlp
- Created: 2018-06-11T09:58:25.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-20T06:25:39.000Z (over 6 years ago)
- Last Synced: 2025-04-03T20:23:10.472Z (about 2 months ago)
- Topics: legal-ai
- Language: Python
- Size: 16.6 KB
- Stars: 128
- Watchers: 9
- Forks: 29
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Few-Shot Charge Prediction with Discriminative Legal Attributes
Source code and datasets of COLING2018 paper: "Few-Shot Charge Prediction with Discriminative Legal Attributes". [(pdf)](http://thunlp.org/~tcc/publications/coling2018_attribute.pdf)
## Dataset
Please download the dataset [here](https://thunlp.oss-cn-qingdao.aliyuncs.com/attribute_charge.zip), 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](http://thunlp.org/~tcc/).