The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
An analysis of active learning strategies for sequence labeling tasks
Open accessAbstract
Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed light on the best active learning approaches for sequence labeling tasks such as information extraction and document segmentation. We survey previously used query selection strategies for sequence models, and propose several novel algorithms to address their shortcomings. We also conduct a large-scale empirical comparison using multiple corpora, which demonstrates that our proposed methods advance the state of the art.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Tjong Kim Sang, E. F., & De Meulder, F. (2003) · Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - · doi:10.3115/1119176.1119195 · Cited by 2,788
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Learning Loss for Active Learning
Yoo, D., & Kweon, I. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00018 · Cited by 740
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606
Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605
Self-citationKnockoff Nets: Stealing Functionality of Black-Box Models
Orekondy, T., Schiele, B., & Fritz, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00509 · Cited by 549
Metadata
- Authors
- Settles, B., & Craven, M.
- Venue
- Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
- Published
- 2008
- Pages
- 1070
- OpenAlex
- W2171671120
- Cited by
- 972
- References
- 3
- FWCI
- 15.63
- Type
- conference paper
- Language
- en
- Open access
- bronze
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