HiEve: A Corpus for Extracting Event Hierarchies from News Stories
By Goran Glavas, Jan Snajder, Marie-Francine Moens, Parisa Kordjamshidi. 2014
65 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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Citations in the corpus
- ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “research by using a trigger word (often a verb or an noun that most clearly describes the event’s occurrence) to represent an event (UzZaman et al., 2013; Glavaš et al., 2014; O’Gorman et al., 2016).”
- Joint Constrained Learning for Event-Event Relation Extraction (2020, methodology): “While data-driven methods provide a general and tractable way for event-event relation extraction, their performance is restricted by the limited annotated resources available (Glavaˇs et al., 2014; Ning et al., 2018b).”
- Temporal Common Sense Acquisition with Minimal Supervision (2020, methodology): “Table 3: Performance on HiEVE.”