The repo includes the code for the following paper:

@inproceedings{li2021sodner,
 title={A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition},
 author={Li, Fei and Lin, Zhichao and Zhang, Meishan and Ji, Donghong},
 booktitle={Proceedings of the ACL},
 year={2021}
}

1. Use "conda" or "virtualenv" to create a virtual python3 environment. Take "conda" as example, run:

conda create -n sodner python=3.6

2. Activate the environment.

conda activate sodner

2. Run the following command to install necessary packages.

pip install -r requirements.txt

3. Download the PyTorch AllenNLP version of SciBERT from here. Put it into the current directory.

4. Put the preprocessed data into "data" directory. There is a sample directory for your reference to preprocess original datasets.

1. Below is the command to run experiments on the sample dataset. If use GPU, change -1 to 0 or other number that is larger than 0.

nohup ./train_sample.sh -1 > sample_0001.log 2>&1 &

1. Run the following command.

cuda_device=-1 allennlp predict models/sample_0001/model.tar.gz data/sample/sample.json --include-package sodner --predictor my_predictor --output-file prediction.txt

1. Change the settings in "sample_working_example.jsonnet" as below.

• debug: true,
• shuffle: false,

2. Add the following environment into your IDE such as PyCharm.

• ie_test_data_path=./data/sample/sample.json;
• ie_dev_data_path=./data/sample/sample.json;
• ie_train_data_path=./data/sample/sample.json;
• cuda_device=-1;

3. Run "debug_sample.py" with debug mode.

1. We show an example to preprocess the CADEC data. First, download the code of Dai et al. 2020. Use their instructions to preprocess the CADEC data and get 3 output files, namely "train.txt", "dev.txt" and "test.txt".

2. Download Stanford CoreNLP. We use "stanford-corenlp-full-2018-10-05".

3. Modify the directory paths at the beginning of "preprocess_cadec.py" based on your environment. Create a "1.sh" file like

#!/bin/bash
sudo /xxx/envs/python37/bin/python "$@"

and run "1.sh preprocess_cadec.py".

We thank all the people that provide their code to help us complete this project. This project is built mainly based on the code published by Wadden et al. 2019.