HiEve: A Corpus for Extracting Event Hierarchies from News Stories

Goran Glavaš, Jan Šnajder, Marie-Francine Moens, Parisa Kordjamshidi


Abstract
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.
Anthology ID:
L14-1020
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3678–3683
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1023_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Goran Glavaš, Jan Šnajder, Marie-Francine Moens, and Parisa Kordjamshidi. 2014. HiEve: A Corpus for Extracting Event Hierarchies from News Stories. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3678–3683, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
HiEve: A Corpus for Extracting Event Hierarchies from News Stories (Glavaš et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1023_Paper.pdf