NCRF++: An Open-source Neural Sequence Labeling Toolkit

Jie Yang, Yue Zhang


Abstract
This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch http://pytorch.org/, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.
Anthology ID:
P18-4013
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
74–79
Language:
URL:
https://aclanthology.org/P18-4013
DOI:
10.18653/v1/P18-4013
Bibkey:
Cite (ACL):
Jie Yang and Yue Zhang. 2018. NCRF++: An Open-source Neural Sequence Labeling Toolkit. In Proceedings of ACL 2018, System Demonstrations, pages 74–79, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
NCRF++: An Open-source Neural Sequence Labeling Toolkit (Yang & Zhang, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-4013.pdf
Code
 jiesutd/NCRFpp +  additional community code
Data
CoNLL-2003Penn Treebank