Semi-Supervised Active Learning for Sequence Labeling

10 years 9 months ago
Semi-Supervised Active Learning for Sequence Labeling
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 semisupervised 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.
Katrin Tomanek, Udo Hahn
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Katrin Tomanek, Udo Hahn
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