2009 SemiSupervisedActiveLearningfor

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Subject Headings: Semi-Supervised Active Learning, Linguistic Sequence Labeling.

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Quotes

Abstract

While Active Learning (AL) has already been shown to markedly reduce the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about the internal structure of the selected sequences (typically, sentences). We propose a semi-supervised AL approach for sequence labeling where only highly uncertain subsequences are presented to human annotators, while all others in the selected sequences are automatically labeled. For the task of entity recognition, our experiments reveal that this approach reduces annotation efforts in terms of manually labeled tokens by up to 60% compared to the standard, fully supervised AL scheme.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2009 SemiSupervisedActiveLearningforKatrin Tomanek
Udo Hahn
Semi-supervised Active Learning for Sequence Labeling2009
AuthorKatrin Tomanek + and Udo Hahn +
titleSemi-supervised Active Learning for Sequence Labeling +
year2009 +