The study and analysis of past events can provide numerous benefits. While event categorization has been previously studied, it usually assigned only one event category to an event. In this study, we focus on multi-label classification for past events, which is a more general and challenging problem than those approached in previous studies. We categorize events into thirteen different types using a range of diverse features and classifiers trained on a dataset that has at least 50 labeled news articles for each category. We have confirmed that using all the features to train classifiers has statistical significance and improves all micro- and macro-average \(F_1\), multi-label accuracy, average precision@5, area under the receiver operating characteristic curve and example-based loss functions.

对过去事件的研究和分析可以提供许多好处。虽然以前已经研究了事件分类，但是通常只将一个事件类别分配给一个事件。在这项研究中，我们专注于过去事件的多标签分类，这是比以前的研究更普遍和更具挑战性的问题。我们使用在数据集上训练的一系列不同功能和分类器，将事件分为13种不同类型，每个数据集至少有50条带标签的新闻文章。我们已经确认，使用所有特征来训练分类器具有统计意义，并可以改善所有微观和宏观平均值\（F_1 \），多标签准确性，平均精度@ 5，接收器工作特性曲线下的面积以及基于示例的损失函数。