Paper: We’re Not in Kansas Anymore: Detecting Domain Changes in Streams

ACL ID D10-1057
Title We’re Not in Kansas Anymore: Detecting Domain Changes in Streams
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Domain adaptation, the problem of adapting a natural language processing system trained in one domain to perform well in a differ- ent domain, has received significant attention. This paper addresses an important problem for deployed systems that has received little at- tention – 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.