Multi-Label Classification for Past Events

Study and analysis of past events can provide numerous benefits. While event categorization has been previously studied, it was usually assigned only one event category to an event. In this work we focus on multi-label classification for past events that is a more general and challenging problem than the previous studies. We categorize them into 13 event categories using a range of diverse features and report micro-average F1 score is improved approximately by 10% compared with the state-of-the-art algorithm.