Learning in the presence of concept drift and hidden contexts
Machine Learning
Text genre classification with genre-revealing and subject-revealing features
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Automatic detection of text genre
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The form is the substance: classification of genres in text
HLTKM '01 Proceedings of the workshop on Human Language Technology and Knowledge Management - Volume 2001
Journal of the American Society for Information Science and Technology
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Confidence estimation for NLP applications
ACM Transactions on Speech and Language Processing (TSLP)
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Sequential Change Detection on Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Active learning with confidence
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Part-of-speech histograms for genre classification of text
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Learning with probabilistic features for improved pipeline models
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Online methods for multi-domain learning and adaptation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
A theory of learning from different domains
Machine Learning
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Automatic domain adaptation for parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Streaming first story detection with application to Twitter
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Domain adaptation meets active learning
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
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Domain adaptation, the problem of adapting a natural language processing system trained in one domain to perform well in a different domain, has received significant attention. This paper addresses an important problem for deployed systems that has received little attention - detecting when such adaptation is needed by a system operating in the wild, i.e., performing classification over a stream of unlabeled examples. Our method uses A-distance, a metric for detecting shifts in data streams, combined with classification margins to detect domain shifts. We empirically show effective domain shift detection on a variety of data sets and shift conditions.