Weichang Liu


2023

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Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs
Jian Liu | Weichang Liu | Yufeng Chen | Jinan Xu | Zhe Zhao
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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.