The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.
Domain Adaptation for Statistical Classifiers
Open accessAbstract
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
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Flagged first, then by citations
2 of the 34 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Bacchiani, M., & Roark, B. (2003). Unsupervised langauge model adaptation. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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Metadata
- Authors
- Daume III, H., & Marcu, D.
- Affiliations
- University of Southern California (US)
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2006-06-21
- Volume
- 26
- Pages
- 101-126
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2120708938
- Cited by
- 816
- References
- 34
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 22 · 2022: 39 · 2021: 51 · 2020: 53 · 2019: 62 · 2018: 48 · 2017: 53 · 2016: 49 · 2015: 47 · 2014: 66 · 2013: 63 · 2012: 60
- FWCI
- 34.24
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Artificial Intelligence Research (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0097846, 0326276)
- Defense 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, statistical classifiers, out-of-domain data, mixture models, maximum entropy classifier, natural language processing, distribution shift
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