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Domain adaptation with structural correspondence learning
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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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