Distinguishing Past, On-going, and Future Events: The EventStatus Corpus

Determining whether a major societal event has already happened, is still on-going, or may occur in the future is crucial for event prediction, timeline generation, and news summarization. We introduce a new task and a new corpus, EventStatus , which has 4500 English and Spanish articles about civil unrest events labeled as P AST , O N -G OING , or F U - TURE . We show that the temporal status of these events is difficult to classify because lo-cal tense and aspect cues are often lacking, time expressions are insufficient, and the linguistic contexts have rich semantic compositionality. We explore two approaches for event status classification: (1) a feature-based SVM classifier augmented with a novel induced lexicon of future-oriented verbs, such as “threat-ened” and “planned”, and (2) a convolutional neural net. Both types of classifiers improve event status recognition over a state-of-the-art TempEval model, and our analysis offers linguistic insights into the semantic composition-ality challenges for this new task.