论文标题
为什么只有micro-f1？关系分类的措施的班级加权
Why only Micro-F1? Class Weighting of Measures for Relation Classification
论文作者
论文摘要
关系分类模型通常仅使用单个量度，例如Micro-F1，Macro-F1或AUC评估。在这项工作中，我们分析了不平衡数据集的加权方案，例如微观和宏。我们介绍了一个用于加权方案的框架，其中现有方案是极端的，以及两个新的中间方案。我们表明，不同加权方案的报告结果更好地突出了模型的优势和劣势。
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for weighting schemes, where existing schemes are extremes, and two new intermediate schemes. We show that reporting results of different weighting schemes better highlights strengths and weaknesses of a model.
