This repository implements an LSTM-CRF model for named entity recognition. The model is same as the one by Lample et al., (2016) except we do not have the last tanh layer after the BiLSTM.
The code provided is used for the paper "Better Modeling of Incomplete Annotation for Named Entity Recognition" published in 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).
NOTE: To extend a more general use case, a PyTorch version is implemented in this repo. The previous implementation using DyNet can be found in first release here. Right now, I have implemented a the "hard" approach as in the paper. "Soft" approach would be coming soon.
Our codebase is built based on the pytorch LSTM-CRF repo.
- PyTorch >= 1.1
- Python 3
Put your dataset under the data folder. You can obtain the conll2003 and conll2002 datasets from other sources. We have put our collected industry datasets ecommerce and youku under the data directory.
Also, put your embedding file under the data directory to run. You need to specify the path for the embedding file.
python3 main.py --embedding_file ${PATH_TO_EMBEDDING} --dataset conll2003 --variant hardChange hard to soft for our soft variant.
(This version actually also supports using contextual representation. But I'm still testing during this weekend.)
- add soft approach
- add other baselines.
If you use this software for research, please cite our paper as follows:
The implementation in our paper is implemented with DyNet. Check out our previous release.
@inproceedings{jie2019better,
title={Better Modeling of Incomplete Annotations for Named Entity Recognition},
author={Jie, Zhanming and Xie, Pengjun and Lu, Wei and Ding, Ruixue and Li, Linlin},
booktitle={Proceedings of NAACL},
year={2019}
}