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)
  • Sequence Tagging
  • Span Classification
  • Boundary Selection
• Relation Extraction (Experimental Results)
• Attribute Extraction
• Machine Translation
• Image Captioning

This repository also maintains the code of our papers:

• Check this link for "Boundary Smoothing for Named Entity Recognition" accepted to ACL 2022 main conference.

$ pip install eznlp

$ python setup.py sdist
$ pip install dist/eznlp-<version>.tar.gz

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

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

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

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

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

@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},
  year={2022},
  publisher={Association for Computational Linguistics},
}

@article{zhu2021framework,
  title={A Unified Framework of Medical Information Annotation and Extraction for {Chinese} Clinical Text},
  author={Zhu, Enwei and Sheng, Qilin and Yang, Huanwan and Li, Jinpeng},
  journal={Working Paper},
  year={2021}
}

• Unify the data interchange format as a dict, i.e., entry
• Reorganize JsonIO
• Memory optimization for large dataset for training PLM
• More relation extraction models
• Multihot classification
• Unify the aggregation interface of pooling and attention
• Radical-level features
• Data augmentation
• Loss increases in later training phases -> LR finder?