Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs
Jian Liu, Weichang Liu, Yufeng Chen, Jinan Xu, Zhe Zhao
Abstract
Real-world named entity recognition (NER) datasets are notorious for their noisy nature, attributed to annotation errors, inconsistencies, and subjective interpretations. Such noises present a substantial challenge for traditional supervised learning methods. In this paper, we present a new and unified approach to tackle annotation noises for NER. Our method considers NER as a constituency tree parsing problem, utilizing a tree-structured Conditional Random Fields (CRFs) with uncertainty evaluation for integration. Through extensive experiments conducted on four real-world datasets, we demonstrate the effectiveness of our model in addressing both partial and incorrect annotation errors. Remarkably, our model exhibits superb performance even in extreme scenarios with 90\% annotation noise.
BibTeX
@inproceedings{
liu2023addressing,
title={Addressing {NER} Annotation Noises with Uncertainty-Guided Tree-Structured {CRF}s},
author={Jian Liu and Weichang Liu and Yufeng Chen and Jinan Xu and Zhe Zhao},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=71Lz8HW3NE}
}