Cited by 816Domain Adaptation and Few-Shot LearningNatural Language Processing TechniquesTopic Modeling

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Domain Adaptation for Statistical Classifiers

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Daume III, H., Marcu, D.·2006·Journal of Artificial Intelligence Research

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Abstract

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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Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
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Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,786
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Inducing features of random fields
Pietra, S. D., Pietra, V. D., & Lafferty, J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.588021 · Cited by 1,034
Multitask Learning: A Knowledge-Based Source of Inductive Bias
Caruana, R. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50012-5 · Cited by 753
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Baxter, J. (2000) · Cited by 730

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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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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)
DOI: 10.1613/jair.1872
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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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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Domain Adaptation for Statistical Classifiers

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks