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2010
ACM

Multi-domain learning by confidence-weighted parameter combination

8 years 9 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources of interest on which the system must perform. For example, a spam filtering system must give high quality predictions for many users, each of whom receives emails from different sources and may make slightly different decisions about what is or is not spam. Rather than learning separate models for each domain, we explore systems that learn across multiple domains. We develop a new multidomain 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 filterin...
Mark Dredze, Alex Kulesza, Koby Crammer
Added 20 May 2011
Updated 20 May 2011
Type Journal
Year 2010
Where ML
Authors Mark Dredze, Alex Kulesza, Koby Crammer
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