Entropy-based training data selection for domain adaptation

Training data selection is a common method for domain adaptation, the goal of which is to choose a subset of training data that works well for a given test set. It has been shown to be effective for tasks such as machine translation and parsing. In this paper, we propose several entropy-based measures for training data selection and test their effectiveness on two tasks: Chinese word segmentation and part-of-speech tagging. The experimental results on the Chinese Penn Treebank indicate that some of the measures provide a statistically significant improvement over random selection for both tasks.