@inproceedings{glavas-etal-2014-hieve,
title = "{H}i{E}ve: A Corpus for Extracting Event Hierarchies from News Stories",
author = "Glava{\v{s}}, Goran and
{\v{S}}najder, Jan and
Moens, Marie-Francine and
Kordjamshidi, Parisa",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1023_Paper.pdf",
pages = "3678--3683",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T HiEve: A Corpus for Extracting Event Hierarchies from News Stories
%A Glavaš, Goran
%A Šnajder, Jan
%A Moens, Marie-Francine
%A Kordjamshidi, Parisa
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F glavas-etal-2014-hieve
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/1023_Paper.pdf
%P 3678-3683
Markdown (Informal)
[HiEve: A Corpus for Extracting Event Hierarchies from News Stories](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1023_Paper.pdf) (Glavaš et al., LREC 2014)
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).