Event Occurrence Date Estimation based on Multivariate Time Series Analysis over Temporal Document Collections

Real world events are quite often mentioned in texts. Estimating the occurrence time of event mentions has many applications in IR, QA, general document understanding and downstream NLP tasks. In this paper we propose an approach to temporal profiling of event mentions in text. Our method utilizes a news article archival collection for collecting temporal as well as textual information containing contemporary and retrospective event references. As we demonstrate in our experiments, the recent method which relies on secondary data sources like Wikipedia is insufficient to correctly estimate the event time, especially, for minor or less well-known events that happened in the past. Our method then harnesses news article archives to effectively infer the occurrence time of past events, and is able to estimate the time at different temporal granularities (e.g., day, week, month, or year). As evidenced through extensive experiments, the proposed model outperforms the existing methods by a large margin at all granularities. We also demonstrate that our approach helps to answer arbitrary questions about past events, when incorporated into a QA framework operating over news article archives.