The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
Domain adaptation with structural correspondence learning
Open accessAbstract
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger.
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“This test is appropriate for binary classification tasks, and has been indeed used in such NLP works (e.g.sentiment classificaiton, (Blitzer et al., 2006;Ziser and Reichart, 2017)).”
From The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing (2018) · cites this paper as Blitzer et al., 2006
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318
Metadata
- Authors
- Blitzer, J., McDonald, R., & Pereira, F.
- Affiliations
- University of Pennsylvania (US)
- ORCID
- Pereira, Fernando
- Venue
- Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06
- Published
- 2006
- Pages
- 120
- OpenAlex
- W2158108973
- Cited by
- 1,543
- References
- 28
- Citations by year
- 2026: 4 · 2025: 25 · 2024: 36 · 2023: 67 · 2022: 70 · 2021: 104 · 2020: 165 · 2019: 123 · 2018: 123 · 2017: 116 · 2016: 99 · 2015: 124 · 2014: 95 · 2013: 86 · 2012: 82
- FWCI
- 19.17
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion)
- Funders
- National Science Foundation (0205448)
- Children's Hospital of Philadelphia
- Defense Advanced Research Projects Agency (NBCHD-03-0010)
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- domain adaptation, part-of-speech tagging, discriminative learning, natural language processing, dependency parsing
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