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NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity
  Recognition

NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition

13 May 2024
Elena Merdjanovska
Ansar Aynetdinov
Alan Akbik
    NoLa
ArXivPDFHTML

Papers citing "NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition"

6 / 6 papers shown
Title
Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations
Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations
David Tschirschwitz
Volker Rodehorst
61
1
0
14 Sep 2024
WRENCH: A Comprehensive Benchmark for Weak Supervision
WRENCH: A Comprehensive Benchmark for Weak Supervision
Jieyu Zhang
Yue Yu
Yinghao Li
Yujing Wang
Yaming Yang
Mao Yang
Alexander Ratner
38
112
0
23 Sep 2021
Learning from Noisy Labels for Entity-Centric Information Extraction
Learning from Noisy Labels for Entity-Centric Information Extraction
Wenxuan Zhou
Muhao Chen
NoLa
29
65
0
17 Apr 2021
FLERT: Document-Level Features for Named Entity Recognition
FLERT: Document-Level Features for Named Entity Recognition
Stefan Schweter
Alan Akbik
37
111
0
13 Nov 2020
Fine-Tuning Pre-trained Language Model with Weak Supervision: A
  Contrastive-Regularized Self-Training Approach
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
Yue Yu
Simiao Zuo
Haoming Jiang
Wendi Ren
T. Zhao
Chao Zhang
AI4MH
23
133
0
15 Oct 2020
Learning to Reweight Examples for Robust Deep Learning
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren
Wenyuan Zeng
Binh Yang
R. Urtasun
OOD
NoLa
107
1,419
0
24 Mar 2018
1