Paper: Semi-Supervised Active Learning for Sequence Labeling

ACL ID P09-1117
Title Semi-Supervised Active Learning for Sequence Labeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

While Active Learning (AL) has already been shown to markedly reduce the anno- tation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about the internal structure of the selected sequences (typ- ically, sentences). We propose a semi- supervised AL approach for sequence la- beling where only highly uncertain sub- sequences are presented to human anno- tators, while all others in the selected se- quences are automatically labeled. For the task of entity recognition, our experiments reveal that this approach reduces annota- tion efforts in terms of manually labeled tokens by up to 60 % compared to the stan- dard, fully supervised AL scheme.