An Encoding Strategy based Word-Character LSTM for Chinese NER

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".

Requirement:

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

Input format:

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   

Pretrained Embeddings:

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

Run:

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

Cite:

@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"
}