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Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple potentially noisy supervision sources.
“Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm”
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Ron Kohavi · 1996
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“Long short-term memory”
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“Combining labeled and unlabeled data with co-training”
A. Blum and Tom. Mitchell · 1998
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“Generating Accurate Rule Sets Without Global Optimization”
Eibe Frank and Ian Witten · 1998
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“Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”
John Lafferty, Andrew McCallum and Fernando Pereira · 2001
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“Training Products of Experts by Minimizing Contrastive Divergence”
Geoffrey. Hinton · 2002
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“Learning question classifiers”
Xin Li and Dan Roth · 2002
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“Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition”
Erik Sang and Fien De · 2003
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“Hidden Markov models and the Baum-Welch algorithm”
Lloyd Welch · 2003
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“SemEval-2014 Task 4: Aspect Based Sentiment Analysis”
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos and Suresh Manandhar · 2004
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“Modeling Annotators: A Generative Approach to Learning from Annotator Rationales”
Omar Zaidan and Jason Eisner · 2008
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“Named entity recognition in wikipedia”
Dominic Balasuriya, Nicky Ringland, Joel Nothman, Tara Murphy and James Curran · 2009
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“Distant supervision for relation extraction without labeled data”
Mike Mintz, Steven Bills, Rion Snow and Daniel Jurafsky · 2009
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“SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals”
Iris Hendrickx, Su Kim, Zornitsa Kozareva, Preslav Nakov, DiarmuidÓ Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano and Stan Szpakowicz · 2010
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“Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data”
Gideon. Mann and A. McCallum · 2010
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“Modeling relations and their mentions without labeled text”
Sebastian Riedel, Limin Yao and Andrew McCallum · 2010
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“Contributions to the study of SMS spam filtering: new collection and results”
Tiago Almeida, Joséía Hidalgo and Akebo Yamakami · 2011
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“Harnessing the Crowdsourcing Power of Social Media for Disaster Relief”
Huiji Gao, Geoffrey Barbier and Rebecca Goolsby · 2011
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“Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations”
R. Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer and Daniel. Weld · 2011
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“Learning Word Vectors for Sentiment Analysis”
Andrew. Maas, Raymond. Daly, Peter. Pham, Dan Huang, Andrew. Ng and Christopher Potts · 2011
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“Ontonotes release 5.0”
Ralph Weischedel, Sameer Pradhan, Lance Ramshaw, Martha Palmer, Nianwen Xue, Mitchell Marcus, Ann Taylor, Craig Greenberg, Eduard Hovy and Robert Belvin · 2011
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“Reducing Wrong Labels in Distant Supervision for Relation Extraction”
Shingo Takamatsu, Issei Sato and H. Nakagawa · 2012
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“Asgard: A portable architecture for multilingual dialogue systems”
Jingjing Liu, Panupong Pasupat, Scott Cyphers and Jim Glass · 2013
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“Query understanding enhanced by hierarchical parsing structures”
Jingjing Liu, Panupong Pasupat, Yining Wang, Scott Cyphers and Jim Glass · 2013
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“NCBI disease corpus: a resource for disease name recognition and concept normalization”
Rezarta Doğan, Robert Leaman and Zhiyong Lu · 2014
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“Improved Pattern Learning for Bootstrapped Entity Extraction”
S. Gupta and Christopher. Manning · 2014
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“TubeSpam: Comment Spam Filtering on YouTube”
T.. Alberto, J.. Lochter and T.. Almeida · 2015
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“Learning From Massive Noisy Labeled Data for Image Classification”
Tong Xiao, Tian Xia, Yi Yang, Chang Huang and Xiaogang Wang · 2015
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“Character-level Convolutional Networks for Text Classification”
Xiang Zhang, Junbo Zhao and Yann LeCun · 2015
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“What do a Million News Articles Look like?”
D. Corney, M. Albakour, Miguel Martinez-Alvarez and Samir Moussa · 2016
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“Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations”
R. Krishna, Yuke Zhu, O. Groth, Justin Johnson, K. Hata, J. Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, D. Shamma, Michael. Bernstein and Li Fei-Fei · 2016
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“BioCreative V CDR task corpus: a resource for chemical disease relation extraction”
Jiao Li, Yueping Sun, Robin Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Davis, Carolyn Mattingly, Thomas Wiegers and Zhiyong Lu · 2016
Earlier work this paper cites.
“End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF”
Xuezhe Ma and Eduard Hovy · 2016
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“Data programming: Creating large training sets, quickly”
Alexander Ratner, Christopher De, Sen Wu, Daniel Selsam and Christopher Ré · 2016
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“Learning the Structure of Generative Models without Labeled Data”
Stephen. Bach, Bryan. He, Alexander. Ratner and C. Ré · 2017
Earlier work this paper cites.
“The comparative toxicogenomics database: update 2017”
Allan Davis, Cynthia Grondin, Robin Johnson, Daniela Sciaky, Benjamin King, Roy McMorran, Jolene Wiegers, Thomas Wiegers and Carolyn Mattingly · 2017
Earlier work this paper cites.
“Swellshark: A generative model for biomedical named entity recognition without labeled data”
Jason Fries, Sen Wu, Alex Ratner and Christopher Ré · 2017
Cited alongside, same era.
“Overview of the BioCreative VI chemical-protein interaction Track”
Martin Krallinger, Obdulia Rabal and Saber Akhondi · 2017
Cited alongside, same era.
“Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach”
Liyuan Liu, Xiang Ren, Qi Zhu, Shi Zhi, Huan Gui, Heng Ji and Jiawei Han · 2017
Cited alongside, same era.
“Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix”
Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan and Dongyan Zhao · 2017
Cited alongside, same era.
“Aggregating and Predicting Sequence Labels from Crowd Annotations”
An Nguyen, Byron Wallace, Junyi Li, Ani Nenkova and Matthew Lease · 2017
Cited alongside, same era.
“Bond: Bert-assisted open-domain named entity recognition with distant supervision”
Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao and Chao Zhang · 2020
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“Named Entity Recognition without Labelled Data: A Weak Supervision Approach”
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin and Samia Touileb · 2020
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“Extracting chemical reactions from text using Snorkel”
Emily. Mallory, Matthieu de Rochemonteix, Alexander. Ratner, Ambika Acharya, Christoper Re, R. Bright and R. Altman · 2020
Later among the works it cites.
“Coresets for Robust Training of Deep Neural Networks against Noisy Labels”
Baharan Mirzasoleiman, Kaidi Cao and Jure Leskovec · 2020
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“Denoising Multi-Source Weak Supervision for Neural Text Classification”
Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell and Chao Zhang · 2020
Later among the works it cites.
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“Snorkel: Rapid training data creation with weak supervision”
Alexander Ratner, Stephen Bach, Henry Ehrenberg, Jason Fries, Sen Wu and Christopher Ré · 2017
Cited alongside, same era.
“Inferring Generative Model Structure with Static Analysis”
P. Varma, Bryan. He, Payal Bajaj, Nishith Khandwala, I. Banerjee, D. Rubin and Christopher Ré · 2017
Cited alongside, same era.
“PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN”
Hanwang Zhang, Zawlin Kyaw, Jinyang Yu and Shih-Fu Chang · 2017
Cited alongside, same era.
“Co-teaching: Robust training of deep neural networks with extremely noisy labels”
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang and Masashi Sugiyama · 2018
Cited alongside, same era.
“Snuba: Automating weak supervision to label training data”
Paroma Varma and Christopher Ré · 2018
Cited alongside, same era.
“Scene graph prediction with limited labels”
Vincent Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re and Li Fei-Fei · 2019
Cited alongside, same era.
“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2019
Cited alongside, same era.
Anna Rogers, Olga Kovaleva and Anna Rumshisky · 2020
Later among the works it cites.
“Weak supervision as an efficient approach for automated seizure detection in electroencephalography”
Khaled Saab, Jared Dunnmon, Christopher Ré, D. Rubin and C. Lee-Messer · 2020
Later among the works it cites.
“Weakly supervised sequence tagging from noisy rules”
Esteban Safranchik, Shiying Luo and Stephen Bach · 2020
Later among the works it cites.
“Learning with Weak Supervision for Email Intent Detection”
Kai Shu, Subhabrata(Subho) Mukherjee, Guoqing Zheng, Ahmed. Awadallah, Milad Shokouhi and Susan Dumais · 2020
Later among the works it cites.
“Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News”
Kai Shu, Guoqing Zheng, Yichuan Li, Subhabrata(Subho) Mukherjee, Ahmed. Awadallah, Scott Ruston and Huan Liu · 2020
Later among the works it cites.
“Nero: A neural rule grounding framework for label-efficient relation extraction”
Wenxuan Zhou, Hongtao Lin, Bill Lin, Ziqi Wang, Junyi Du, Leonardo Neves and Xiang Ren · 2020
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“Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling”
Benedikt Boecking, Willie Neiswanger, Eric Xing and Artur Dubrawski · 2021
Closest in time.
“Dependency Structure Misspecification in Multi-Source Weak Supervision Models”
Salvaühling Cachay, Benedikt Boecking and Artur Dubrawski · 2021
Closest in time.
“NATCAT: Weakly Supervised Text Classification with Naturally Annotated Resources”
Zewei Chu, Karl Stratos and Kevin Gimpel · 2021
Closest in time.
“Ontology-driven weak supervision for clinical entity classification in electronic health records”
Jason Fries, E. Steinberg, S. Khattar, S. Fleming, J. Posada, A. Callahan and N. Shah · 2021
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“Adaptive Rule Discovery for Labeling Text Data”
Sainyam Galhotra, Behzad Golshan and Wang-Chiew Tan · 2021
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“Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems”
Karan Goel, Laurel. Orr, Nazneen Rajani, Jesse Vig and Christopher Ré · 2021
Closest in time.
“Analysing the Noise Model Error for Realistic Noisy Label Data”
Michael Hedderich, Dawei Zhu and Dietrich Klakow · 2021
Closest in time.
“Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data”
Chenghao Jia, Yongliang Shen, Yechun Tang, Lu Sun and Weiming Lu · 2021
Closest in time.
“Self-Training with Weak Supervision”
Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng and Ahmed Awadallah · 2021
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“Weakly Supervised Named Entity Tagging with Learnable Logical Rules”
Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley and Zhe Feng · 2021
Closest in time.
“MoPro: Webly Supervised Learning with Momentum Prototypes”
Junnan Li, Caiming Xiong and Steven Hoi · 2021
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“BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition”
Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang and Le Song · 2021
Closest in time.
“skweak: Weak Supervision Made Easy for NLP”
Pierre Lison, Jeremy Barnes and Aliaksandr Hubin · 2021
Closest in time.
“Noisy-Labeled NER with Confidence Estimation”
Kun Liu, Yao Fu, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang and Sheng Gao · 2021
Closest in time.
“Semi-Supervised Data Programming with Subset Selection”
Ayush Maheshwari, Oishik Chatterjee, Krishnateja Killamsetty, Ganesh Ramakrishnan and Rishabh Iyer · 2021
Closest in time.
“DeFraudNet: An End-to-End Weak Supervision Framework to Detect Fraud in Online Food Delivery”
Jose Mathew, Meghana Negi, Rutvik Vijjali and Jairaj Sathyanarayana · 2021
Closest in time.
“Adversarial Multiclass Learning under Weak Supervision with Performance Guarantees”
A. Mazzetto, C. Cousins, D. Sam, S.. Bach and E. Upfal · 2021
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“Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees”
A. Mazzetto, D. Sam, A. Park, E. Upfal and S.. Bach · 2021
Closest in time.
“Search4Code: Code Search Intent Classification Using Weak Supervision”
Nikitha Rao, Chetan Bansal and Joe Guan · 2021
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“Visual Distant Supervision for Scene Graph Generation”
Yuan Yao, Ao Zhang, Xu Han, Mengdi Li, Cornelius Weber, Zhiyuan Liu, Stefan Wermter and Maosong Sun · 2021
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“Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach”
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao and Chao Zhang · 2021
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“QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction”
Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao and Qiang Yang · 2021
Closest in time.
“GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition”
Xinyan Zhao, Haibo Ding and Zhe Feng · 2021
Closest in time.
“Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition”
Erik. Tjong · 2024
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“Learning named entity tagger using domain-specific dictionary”
Jingbo Shang, Liyuan Liu, Xiaotao Gu, Xiang Ren, Teng Ren and Jiawei Han · 2064
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