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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
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Self-citationKnockoff Nets: Stealing Functionality of Black-Box Models
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Cost-effective Batch-mode Multi-label Active Learning
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Confidence Estimation for Object Detection in Document Images
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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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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
