poster

A survey of noise reduction methods for distant supervision

Authors:

Michael Wiegand,

Dietrich KlakowAuthors Info & Claims

AKBC '13: Proceedings of the 2013 workshop on Automated knowledge base construction

Pages 73 - 78

https://doi.org/10.1145/2509558.2509571

Published: 27 October 2013 Publication History

Abstract

We survey recent approaches to noise reduction in distant supervision learning for relation extraction. We group them according to the principles they are based on: at-least-one constraints, topic-based models, or pattern correlations. Besides describing them, we illustrate the fundamental differences and attempt to give an outlook to potentially fruitful further research. In addition, we identify related work in sentiment analysis which could profit from approaches to noise reduction.

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Cited By

Li JFeng SChiu B(2024)Few-Shot Relation Extraction With Dual Graph Neural Network InteractionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327893835:10(14396-14408)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3278938
Zhu XZhang YChen LChen K(2024)Triple-D: Denoising Distant Supervision for High-Quality Data Creation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00151(1874-1887)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00151
Ristoski P(2023)Web MiningMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_20(447-467)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_20
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Index Terms

A survey of noise reduction methods for distant supervision
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Conferences

AKBC '13: Proceedings of the 2013 workshop on Automated knowledge base construction

October 2013

124 pages

ISBN:9781450324113

DOI:10.1145/2509558

Program Chairs:
Fabian M. Suchanek
Max Planck Institute for Informatics, Germany
,
Sebastian Riedel
University College London, UK
,
Sameer Singh
University of Massachusetts Amherst, USA
,
Partha Pratim Talukdar
Carnegie Mellon University, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - 28, 2013

California, San Francisco, USA

Acceptance Rates

AKBC '13 Paper Acceptance Rate 9 of 19 submissions, 47%;

Overall Acceptance Rate 9 of 19 submissions, 47%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Li JFeng SChiu B(2024)Few-Shot Relation Extraction With Dual Graph Neural Network InteractionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327893835:10(14396-14408)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3278938
Zhu XZhang YChen LChen K(2024)Triple-D: Denoising Distant Supervision for High-Quality Data Creation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00151(1874-1887)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00151
Ristoski P(2023)Web MiningMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_20(447-467)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_20
Li FChen GLiu X(2022)An event-based automatic annotation method for datasets of interpersonal relation extractionApplied Intelligence10.1007/s10489-022-03547-853:3(2629-2639)Online publication date: 11-May-2022
https://doi.org/10.1007/s10489-022-03547-8
Frisoni GMoro GCarbonaro A(2021)A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature WaveIEEE Access10.1109/ACCESS.2021.31309569(160721-160757)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3130956
Tessore JEsnaola LLanzarini LBaldassarri S(2021)Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in SpanishCognitive Computation10.1007/s12559-020-09800-xOnline publication date: 18-Jan-2021
https://doi.org/10.1007/s12559-020-09800-x
Han JWang H(2021)Improving Open Information Extraction with Distant Supervision LearningNeural Processing Letters10.1007/s11063-021-10548-0Online publication date: 4-Jun-2021
https://doi.org/10.1007/s11063-021-10548-0
Su PLi GWu CVijay-Shanker K(2019)Using distant supervision to augment manually annotated data for relation extractionPLOS ONE10.1371/journal.pone.021691314:7(e0216913)Online publication date: 30-Jul-2019
https://doi.org/10.1371/journal.pone.0216913
Smirnova ACudré-Mauroux P(2018)Relation Extraction Using Distant SupervisionACM Computing Surveys10.1145/324174151:5(1-35)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3241741
Lourentzou IGruhl DWelch SChampin PGandon FMédini LLalmas MIpeirotis P(2018)Exploring the Efficiency of Batch Active Learning for Human-in-the-Loop Relation ExtractionCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191546(1131-1138)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191546
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