/supervised-oie

Code for training a Neural Open IE model (NAACL2018)

Primary LanguagePythonMIT LicenseMIT

Deprecated!

The maintenance of this project has moved to the AllenNLP framework.
Over at the models page you can find train and prediction instructions, as well as an online demo.

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supervised-oie

Code for training a supervised Neural Open IE model, as described in our NAACL2018 paper.
🚧 Still under construction 🚧

Citing 🔖

If you use this software, please cite:

@InProceedings{Stanovsky2018NAACL,
  author    = {Gabriel Stanovsky and Julian Michael and Luke Zettlemoyer and Ido Dagan},
  title     = {Supervised Open Information Extraction},
  booktitle = {Proceedings of The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {(to appear)},
}

Quickstart 🐣

  1. Install requirements 🙇
pip install requirements.txt
  1. Download embeddings 🚶
cd ./pretrained_word_embeddings/
./download_external.sh
  1. Train model 🏃
cd ./src
python  ./rnn/confidence_model.py  --train=../data/train.conll  --dev=../data/dev.conll  --test=../data/test.conll --load_hyperparams=../hyerparams/confidence.json```

NOTE: Models are saved by default to the models dir, unless a "--saveto" command line argument is passed. See confidence_model.py for more details.

  1. Predict with a trained model 👏
python ./trained_oie_extractor.py \
    --model=path/to/model \
    --in=path/to/raw/sentences
    --out=path/to/output/file
    --conll

More scripts 🚴

See src/scripts for more handy scripts. Additional documentation coming soon!