/nested-ner-tacl2020-transformers

Implementation of Nested Named Entity Recognition using BERT

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Implementation of Nested Named Entity Recognition

Some files are part of NeuroNLP2.

Requirements

We tested this library with the following libraries:

Running experiments

Testing this library with a sample data

  1. Run the gen_data.py to generate the processed data files for training, and they will be placed at the "./data/" directory
    python gen_data.py
  2. Run the train.py to start training
    python train.py

Reproducing our experiment on the ACE-2004 dataset

  1. Put the corpus ACE-2004 into the "../ACE2004/" directory
  2. Put this .tgz file into the "../" and extract it
  3. Run the parse_ace2004.py to extract sentences for training, and they will be placed at the "./data/ace2004/"
    python parse_ace2004.py
  4. Run the gen_data_for_ace2004.py to prepare the processed data files for training, and they will be placed at the "./data/" directory
    python gen_data_for_ace2004.py
  5. Run the train.py to start training
    python train.py

Reproducing our experiment on the ACE-2005 dataset

  1. Put the corpus ACE-2005 into the "../ACE2005/" directory
  2. Put this .tgz file into the "../" and extract it
  3. Run the parse_ace2005.py to extract sentences for training, and they will be placed at the "./data/ace2005/"
    python parse_ace2005.py
  4. Run the gen_data_for_ace2005.py to prepare the processed data files for training, and they will be placed at the "./data/" directory
    python gen_data_for_ace2005.py
  5. Run the train.py to start training
    python train.py

Reproducing our experiment on the GENIA dataset

  1. Put the corpus GENIA into the "../GENIA/" directory
  2. Run the parse_genia.py to extract sentences for training, and they will be placed at the "./data/genia/"
    python parse_genia.py
  3. Run the gen_data_for_genia.py to prepare the processed data files for training, and they will be placed at the "./data/" directory
    python gen_data_for_genia.py
  4. Run the train.py to start training
    python train.py

Configuration

Configurations of the model and training are in config.py

Citation

Please cite our paper:

@article{shibuya-hovy-2020-nested,
  title = "Nested Named Entity Recognition via Second-best Sequence Learning and Decoding",
  author = "Shibuya, Takashi and Hovy, Eduard",
  journal = "Transactions of the Association for Computational Linguistics",
  volume = "8",
  year = "2020",
  doi = "10.1162/tacl_a_00334",
  pages = "605--620",
}