An model take both character and words as input for Chinese NER task.

Models and results can be found at our NAACL 2019 paper "An Encoding Strategy based Word-Character LSTM for Chinese NER". It achieves state-of-the-art performance on most of the dataset.

Most of the code is written with reference to Yang Jie's "NCRF++". To know more about "NCRF++", please refer to the paper "NCRF++: An Open-source Neural Sequence Labeling Toolkit".

Python 3.6
Pytorch: 0.4.0

if you want to use the tensorboard in our code, you should also install the followings:
tensorboardX 1.2
tensorflow 1.6.0

CoNLL format (prefer BIOES tag scheme), with each character its label for one line. Sentences are splited with a null line.

美   B-LOC  
国   E-LOC  
的   O  
华   B-PER  
莱   I-PER  
士   E-PER  

我   O  
跟   O  
他   O  
谈   O  
笑   O  
风   O  
生   O   

Character embeddings: gigword_chn.all.a2b.uni.ite50.vec
Word embeddings: ctb.50d.vec

put each dataset to the data dir, and then simply run the .py file.
For example, to run Weibo experiment, just run: python3 weibo.py

@inproceedings{liu-etal-2019-encoding,
title = "An Encoding Strategy Based Word-Character {LSTM} for {C}hinese {NER}",
author = "Liu, Wei and Xu, Tongge and Xu, Qinghua and Song, Jiayu and Zu, Yueran",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1247",
pages = "2379--2389"
}