Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the question of how well performance metrics computed on noisy, historical data reflect the performance on the intended future machine learning model input. The results demonstrate the utility of dirty training datasets used to build prediction models for cleaner (and different) prediction inputs.
翻译:用于文本分类的产业数据集很少为此目的创建,在大多数情况下,数据和目标预测是累积历史数据的副产品,通常充满噪音,存在于基于文本的文件和有针对性的标签中,在这项工作中,我们讨论了在噪音和历史数据上计算的业绩尺度如何反映未来机器学习模型投入的绩效的问题。结果显示,用于为更清洁(和不同)预测投入建立预测模型的肮脏培训数据集很有用。