2000
DOI: 10.1007/3-540-40030-3_18
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Improving Learning by Choosing Examples Intelligently in Two Natural Language Tasks

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Cited by 5 publications
(4 citation statements)
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“…In many real-world applications, obtaining labeled samples is very costly, while a large number of unlabeled samples are readily available. To exploit unlabeled samples and improve the accuracies of learners, AL and SSL have been extensively investigated for many real-world problems in machine learning, such as text classification (Tong and Chang, 2001;Hoi et al, 2006;Burkhardt et al, 2018), information extraction (Thompson et al, 1999;Wu and Pottenger, 2005), image classification and retrieval (Hoi and Lyu, 2005;Li et al, 2013;Pedronette et al, 2019), and cancer diagnosis (Nguyen et al, 2020;Menon et al, 2020).…”
Section: Related Workmentioning
confidence: 99%
“…In many real-world applications, obtaining labeled samples is very costly, while a large number of unlabeled samples are readily available. To exploit unlabeled samples and improve the accuracies of learners, AL and SSL have been extensively investigated for many real-world problems in machine learning, such as text classification (Tong and Chang, 2001;Hoi et al, 2006;Burkhardt et al, 2018), information extraction (Thompson et al, 1999;Wu and Pottenger, 2005), image classification and retrieval (Hoi and Lyu, 2005;Li et al, 2013;Pedronette et al, 2019), and cancer diagnosis (Nguyen et al, 2020;Menon et al, 2020).…”
Section: Related Workmentioning
confidence: 99%
“…A general framework for tackling the labeled data acquisition problem is active learning, in which the learner strategically chooses the most valuable instances as opposed to selecting a random sample for labeling (Thompson, Califf, and Mooney 1999). Three main active learning settings have been considered in the literature: membership query synthesis, stream-based selective sampling, and pool-based sampling.…”
Section: Introductionmentioning
confidence: 99%
“…2016b), and semantic parsing (Thompson et al . 1999). AL has been used to tackle the label acquisition problem in the NER task as well.…”
Section: Introductionmentioning
confidence: 99%
“…The basic idea of this approach is starting with a small set of annotated examples and a large set of unannotated examples, the learner attempts to select additional examples for annotation that are likely to be the most useful. Some works that have applied selective sampling to IE pattern acquisition are (Soderland 1999), (Thompson et al 1999) and (Turmo 2002).…”
Section: Supervised Machine Learning Approachesmentioning
confidence: 99%