Implementation and experiments for "Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss" to appear in TACL 2021.

Experiments were all run on a p3.2xlarge instance on AWS with the amazon deep learning AMI on ubuntu 18 using python 3.7. (You'll need at least 16GB of GPU ram to run these experiments as is.)

Install the required environment with: conda env create -f environment.yml

Then activate with: conda activate env

The CoNLL 2003 data from Mayhew et al. 19 is already in the data/ dir, but the ontonotes5 data must be created programmatically after you place the corpus directory into the data/ dir because of licensing restrictions.

Then in this order run the commands:

Convert the original Ontonotes5 corpus dataset format to ours

bash scripts/process_ontonotes_data.sh

Create the vocab files

bash scripts/make_vocabs.sh

Downsample the datasets

bash scripts/sample_datasets.sh

Published experiments can all be run using the bash scripts in scripts/benchmark_runs, scripts/eer_variation_runs, and scripts/learning_curve_runs.

For example:

cd ner-expected-entity-ratio
conda activate env
bash scripts/benchmark_runs/run_o_bias_experiments.sh

Many of the experiments were done in parallel using remote nodes, so either this process needs to be reproduced, or the respective run_remote_ml_experiment invocations need to be converted to run_ml_experiment commands by removing the remote parameters.

Also, we've left out the codebases for the baselines (Mayhew et al. '19, Li et al. '21) because they aren't our works but if you need these let me know.

• data: directory housing data. conll data already provided
• experiments: contains base allennlp config files for running experiments and is where runs are logged
• ml: source code
• scripts: bash scripts for running experiments

If you make use of this code or information from the paper please cite:

@article{Effland2021PartiallySN,
  title={Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss},
  author={Thomas Effland and Michael Collins},
  journal={Transactions of the Association for Computational Linguistics},
  year={2021},
  volume={9},
  pages={1320-1335}
}