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1807.00745
Cited By

Training a Neural Network in a Low-Resource Setting on Automatically
Annotated Noisy Data

v1v2 (latest)

Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

2 July 2018

Michael A. Hedderich

Dietrich Klakow

ArXiv (abs)PDF HTML

Papers citing "Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data"

15 / 15 papers shown

Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints

Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints

134

0

0

02 Sep 2025

Deep Vision-Based Framework for Coastal Flood Prediction Under Climate
Change Impacts and Shoreline Adaptations

Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations

Aaron Chung Hin Chow

204

3

0

06 Jun 2024

Re-Examine Distantly Supervised NER: A New Benchmark and a Simple
Approach

Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach

Qi Li

321

4

0

22 Feb 2024

An Effective Approach for Multi-label Classification with Missing Labels

An Effective Approach for Multi-label Classification with Missing Labels

227

8

0

24 Oct 2022

Low Resource Pipeline for Spoken Language Understanding via Weak
Supervision

Low Resource Pipeline for Spoken Language Understanding via Weak Supervision

156

1

0

21 Jun 2022

ULF: Unsupervised Labeling Function Correction using Cross-Validation
for Weak Supervision

ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak SupervisionConference on Empirical Methods in Natural Language Processing (EMNLP), 2022

Anastasiia Sedova

381

2

0

14 Apr 2022

Noisy-Labeled NER with Confidence Estimation

Noisy-Labeled NER with Confidence EstimationNorth American Chapter of the Association for Computational Linguistics (NAACL), 2021

Chuanqi Tan

Ningyu Zhang

272

65

0

09 Apr 2021

Analysing the Noise Model Error for Realistic Noisy Label Data

Analysing the Noise Model Error for Realistic Noisy Label DataAAAI Conference on Artificial Intelligence (AAAI), 2021

Michael A. Hedderich

Dietrich Klakow

330

25

0

24 Jan 2021

A Survey on Recent Approaches for Natural Language Processing in
Low-Resource Scenarios

A Survey on Recent Approaches for Natural Language Processing in Low-Resource ScenariosNorth American Chapter of the Association for Computational Linguistics (NAACL), 2020

Michael A. Hedderich

Jannik Strötgen

Dietrich Klakow

730

366

0

23 Oct 2020

Transfer Learning and Distant Supervision for Multilingual Transformer
Models: A Study on African Languages

Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages

Michael A. Hedderich

David Ifeoluwa Adelani

Jesujoba Oluwadara Alabi

Dietrich Klakow

270

79

0

07 Oct 2020

NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy
Channel for Robust Pharmacological Entity Detection

NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection

Jannik Strötgen

287

6

0

02 Jul 2020

Distant Supervision and Noisy Label Learning for Low Resource Named
Entity Recognition: A Study on Hausa and Yorùbá

Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá

David Ifeoluwa Adelani

Michael A. Hedderich

Esther van den Berg

Dietrich Klakow

240

12

0

18 Mar 2020

Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with
Noisy Labels

Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy LabelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2019

Michael A. Hedderich

Dietrich Klakow

261

15

0

14 Oct 2019

Named Entity Recognition with Partially Annotated Training Data

Named Entity Recognition with Partially Annotated Training DataConference on Computational Natural Language Learning (CoNLL), 2019

Stephen D. Mayhew

Snigdha Chaturvedi

252

51

0

20 Sep 2019

Handling Noisy Labels for Robustly Learning from Self-Training Data for
Low-Resource Sequence Labeling

Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling

Michael A. Hedderich

Dietrich Klakow

198

18

0

28 Mar 2019

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