HSCRF achieves F1 score of 91.38+/-0.10 on the CoNLL 2003 NER dataset, without using any additional corpus or resource.
The code is based on PyTorch. You can find installation instructions here.
The code is written in Python 2.7 and pytorch 0.2.0. Its dependencies are summarized in the file requirements.txt. You can install these dependencies like this:
pip install -r requirements.txt
CUDA_VISIBLE_DEVICES=0 python train.py --char_lstm --high_way
Glove: You can find the pre-trained word embedding here,
and place glove.6B.100d.txt in data/.
If you use the code, please cite the following paper: "Hybrid semi-Markov CRF for Neural Sequence Labeling" Zhi-Xiu Ye, Zhen-Hua Ling. ACL (2018)
@InProceedings{HSCRF,
author = "Ye, Zhixiu
and Ling, Zhen-Hua",
title = "Hybrid semi-Markov CRF for Neural Sequence Labeling",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "235--240",
location = "Melbourne, Australia",
url = "http://aclweb.org/anthology/P18-2038"
}