Semi-Supervised Active Learning for Sequence Labeling
By Katrin Tomanek, Udo Hahn. 2009
109 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yuji Roh
- Steven Euijong Whang
- Geon Heo
- Xuan Zhu
- Björn Schuller
- Florian Eyben
- Maja Pantic
- Anton Batliner
Citations in the corpus
- A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Tomanek and Hahn [112] propose semi-supervised active learning (SeSAL) for sequence labeling tasks, which include POS tagging, chunking, or named entity recognition (NER).”
- Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments (2016, methodology): “Certainty-based AL has been also used alongside self-training to significantly reduce the human labeling effort in spoken language understanding [31] and natural language processing [38].”