HiEve: A Corpus for Extracting Event Hierarchies from News Stories
Goran Glavaš, Jan Šnajder, Marie‐Francine Moens, Parisa Kordjamshidi · 2014
In news stories, event mentions denote real-world events of different spatial and temporal granularity.Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents.In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events.In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent-subevent relations).We describe the process of manual annotation of HiEve.Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research.Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.