Easy Natural Language Processing

Overparameterized neural networks are lazy (Chizat et al., 2019), so we design structures and objectives that can be easily optimized.

eznlp is a PyTorch-based package for neural natural language processing, currently supporting the following tasks:

• Text Classification (Experimental Results)
• Named Entity Recognition (Experimental Results)
• Relation Extraction (Experimental Results)
• Attribute Extraction
• Machine Translation
• Image Captioning

This repository also maintains the code of our papers:

• Check this link for "Deep Span Representations for Named Entity Recognition" accepted to Findings of ACL 2023.
• Check this link for "Boundary Smoothing for Named Entity Recognition" in ACL 2022.
• Check the annotation scheme and HwaMei-500 dataset described in "A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text" on Artificial Intelligence in Medicine.

Installation

Create an environment

$ conda create --name eznlp python=3.8
$ conda activate eznlp

Install dependencies

$ conda install numpy=1.18.5 pandas=1.0.5 xlrd=1.2.0 matplotlib=3.2.2 
$ conda install pytorch=1.7.1 torchvision=0.8.2 torchtext=0.8.1 {cpuonly|cudatoolkit=10.2|cudatoolkit=11.0} -c pytorch 
$ pip install -r requirements.txt 

Install eznlp

• From source (recommended)

$ python setup.py sdist
$ pip install dist/eznlp-<version>.tar.gz --no-deps

• With pip

$ pip install eznlp --no-deps

Running the Code

Text classification

$ python scripts/text_classification.py --dataset <dataset> [options]

Entity recognition

$ python scripts/entity_recognition.py --dataset <dataset> [options]

Relation extraction

$ python scripts/relation_extraction.py --dataset <dataset> [options]

Attribute extraction

$ python scripts/attribute_extraction.py --dataset <dataset> [options]

Citation

If you find our code useful, please cite the following papers:

@inproceedings{zhu2023deep,
  title={Deep Span Representations for Named Entity Recognition},
  author={Zhu, Enwei and Liu, Yiyang and Li, Jinpeng},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
  month={jul},
  year={2023},
  address={Toronto, Canada},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2023.findings-acl.672},
  doi={10.18653/v1/2023.findings-acl.672},
  pages={10565--10582}
}

@inproceedings{zhu2022boundary,
  title={Boundary Smoothing for Named Entity Recognition},
  author={Zhu, Enwei and Li, Jinpeng},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month={may},
  year={2022},
  address={Dublin, Ireland},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2022.acl-long.490},
  doi={10.18653/v1/2022.acl-long.490},
  pages={7096--7108}
}

@article{zhu2023framework,
  title={A unified framework of medical information annotation and extraction for {C}hinese clinical text},
  author={Zhu, Enwei and Sheng, Qilin and Yang, Huanwan and Liu, Yiyang and Cai, Ting and Li, Jinpeng},
  journal={Artificial Intelligence in Medicine},
  volume={142},
  pages={102573},
  year={2023},
  publisher={Elsevier}
}

References

• Chizat, L., Oyallon, E., and Bach, F. On lazy training in differentiable programming. In NeurIPS 2019.