Online methods for multi-domain learning and adaptation

Authors:
Mark Dredze;Koby Crammer
Affiliations:
University of Pennsylvania, Philadelphia, PA;University of Pennsylvania, Philadelphia, PA
Venue:
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Year:
2008

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Abstract

NLP tasks are often domain specific, yet systems can learn behaviors across multiple domains. We develop a new multi-domain online learning framework based on parameter combination from multiple classifiers. Our algorithms draw from multi-task learning and domain adaptation to adapt multiple source domain classifiers to a new target domain, learn across multiple similar domains, and learn across a large number of disparate domains. We evaluate our algorithms on two popular NLP domain adaptation tasks: sentiment classification and spam filtering.