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Most crowdsourcing learning methods treat disagreement between annotators as noisy labelings while inter-disagreement among experts is often a good indicator for the ambiguity and uncertainty that is inherent in natural language.
A crowdsourced frame disambiguation corpus with ambiguity
Dumitrache, A.; Aroyo, L.; and Welty, C. 2019 · 1904
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Maximum likelihood estimation of observer error-rates using the EM algorithm
Dawid, A. P.; and Skene, A. M. 1979 · 1979
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Hidden Markov models for speech recognition
Juang, B. H.; and Rabiner, L. R. 1991 · 1991
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Understanding the metropolis-hastings algorithm
Chib, S.; and Greenberg, E. 1995 · 1995
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
Lafferty, J.; McCallum, A.; and Pereira, F. C. 2001 · 2001
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Sang, E. F.; and De Meulder, F. 2003 · 2003
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Finding scientific topics
Griffiths, T. L.; and Steyvers, M. 2004 · 2004
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Semi-markov conditional random fields for information extraction
Sarawagi, S.; and Cohen, W. W. 2004 · 2004
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Cheap and fast–but is it good? evaluating non-expert annotations for natural language tasks
Snow, R.; O’connor, B.; Jurafsky, D.; and Ng, A. Y. 2008 · 2008
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Annotating named entities in twitter data with crowdsourcing
Finin, T.; Murnane, W.; Karandikar, A.; Keller, N.; Martineau, J.; and Dredze, M. 2010 · 2010
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Learning from crowds
Raykar, V. C.; Yu, S.; Zhao, L. H.; Valadez, G. H.; Florin, C.; Bogoni, L.; and Moy, L. 2010 · 2010
Cited alongside, same era.
Learning from crowds in the presence of schools of thought
Tian, Y.; and Zhu, J. 2012 · 2012
Cited alongside, same era.
Sembler: Ensembling crowd sequential labeling for improved quality
Wu, X.; Fan, W.; and Yu, Y. 2012 · 2012
Cited alongside, same era.
Learning whom to trust with MACE
Hovy, D.; Berg-Kirkpatrick, T.; Vaswani, A.; and Hovy, E. 2013 · 2013
Cited alongside, same era.
Measuring crowd truth: Disagreement metrics combined with worker behavior filters
Soberón, G.; Aroyo, L.; Welty, C.; Inel, O.; Lin, H.; and Overmeen, M. 2013 · 2013
Cited alongside, same era.
Sequence labeling with multiple annotators
Rodrigues, F.; Pereira, F.; and Ribeiro, B. 2014 · 2014
Cited alongside, same era.
Aggregating and predicting sequence labels from crowd annotations
Nguyen, A. T.; Wallace, B. C.; Li, J. J.; Nenkova, A.; and Lease, M. 2017 · 2017
Later among the works it cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Multi-label learning from crowds
Li, S.-Y.; Jiang, Y.; Chawla, N. V.; and Zhou, Z.-H. 2018 · 2018
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A Bayesian approach for sequence tagging with crowds
Simpson, E.; and Gurevych, I. 2018 · 2018
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Multi-label inference for crowdsourcing
Zhang, J.; and Wu, X. 2018 · 2018
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Multi-label crowd consensus via joint matrix factorization
Tu, J.; Yu, G.; Domeniconi, C.; Wang, J.; Xiao, G.; and Guo, M. 2020 · 2020
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Bidirectional LSTM-CRF models for sequence tagging
Huang, Z.; Xu, W.; and Yu, K. 2015 · 2015
Cited alongside, same era.
A bayesian framework for modeling human evaluations
Lakkaraju, H.; Leskovec, J.; Kleinberg, J.; and Mullainathan, S. 2015 · 2015
Cited alongside, same era.
Diversified hidden Markov models for sequential labeling
Qiao, M.; Bian, W.; Da Xu, R. Y.; and Tao, D. 2015 · 2015
Cited alongside, same era.
Crf-cnn: Modeling structured information in human pose estimation
Chu, X.; Ouyang, W.; Li, H.; and Wang, X. 2016 · 2016
Cited alongside, same era.
Learning part-of-speech taggers with inter-annotator agreement loss
Plank, B.; Hovy, D.; and Søgaard, A. 2014a
Cited in the paper.
Linguistically debatable or just plain wrong?
Plank, B.; Hovy, D.; and Søgaard, A. 2014b
Cited in the paper.
Later among the works it cites.
Active multilabel crowd consensus
Yu, G.; Tu, J.; Wang, J.; Domeniconi, C.; and Zhang, X. 2020 · 2020
Later among the works it cites.
Truth discovery in sequence labels from crowds
Sabetpour, N.; Kulkarni, A.; Xie, S.; and Li, Q. 2021 · 2021
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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
Whitehill, J.; Wu, T.-f.; Bergsma, J.; Movellan, J.; and Ruvolo, P. 2009 · 2043
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