This paper adds to the ongoing discussion in the natural language processing community on how to choose a good development set. Motivated by the real-life necessity of applying machine learning models to different data distributions, we propose a clustering-based data splitting algorithm. It creates development (or test) sets which are lexically different from the training data while ensuring similar label distributions. Hence, we are able to create challenging cross-validation evaluation setups while abstracting away from performance differences resulting from label distribution shifts between training and test data. In addition, we present a Python-based tool for analyzing and visualizing data split characteristics and model performance. We illustrate the workings and results of our approach using a sentiment analysis and a patent classification task.
This study examines structural differences in the subjective quality of health care in Germany using a newspaper survey. We find that there are significant differences between urban and rural areas as well as between public and private insurance. In rural areas, the provision of general practitioners, specialists and hospitals are considered as worse than in cities. In particular, public insured individuals asses the provision of specialized doctors and hospitals as lower than private insured and criticize long waiting times for appointments and lacking coverage of health care costs by the statutory health insurance.
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