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Problems stemming from domain adaptation continue to plague the statistical natural language processing community. There has been continuing work trying to find general purpose algorithms to alleviate this problem. In this paper we argue that existing general purpose approaches usually only focus on one of two issues related to the difficulties faced by adaptation: 1) difference in base feature statistics or 2) task differences that can be detected with labeled data. We argue that it is necessary to combine these two classes of adaptation algorithms, using evidence collected through theoretical analysis and simulated and real-world data experiments. We find that the combined approach often outperforms the individual adaptation approaches. By combining simple approaches from each class of adaptation algorithm, we achieve state-of-the-art results for both Named Entity Recognition adaptation task and the Preposition Sense Disambiguation adaptation task. Second, we also show that applying an adaptation algorithm that finds shared representation between domains often impacts the choice in adaptation algorithm that makes use of target labeled data.