@inproceedings{araki-etal-2014-detecting,
title = "Detecting Subevent Structure for Event Coreference Resolution",
author = "Araki, Jun and
Liu, Zhengzhong and
Hovy, Eduard and
Mitamura, Teruko",
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/963_Paper.pdf",
abstract = "In the task of event coreference resolution, recent work has shown the need to perform not only full coreference but also partial coreference of events. We show that subevents can form a particular hierarchical event structure. This paper examines a novel two-stage approach to finding and improving subevent structures. First, we introduce a multiclass logistic regression model that can detect subevent relations in addition to full coreference. Second, we propose a method to improve subevent structure based on subevent clusters detected by the model. Using a corpus in the Intelligence Community domain, we show that the method achieves over 3.2 BLANC F1 gain in detecting subevent relations against the logistic regression model.",
}
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%0 Conference Proceedings
%T Detecting Subevent Structure for Event Coreference Resolution
%A Araki, Jun
%A Liu, Zhengzhong
%A Hovy, Eduard
%A Mitamura, Teruko
%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 araki-etal-2014-detecting
%X In the task of event coreference resolution, recent work has shown the need to perform not only full coreference but also partial coreference of events. We show that subevents can form a particular hierarchical event structure. This paper examines a novel two-stage approach to finding and improving subevent structures. First, we introduce a multiclass logistic regression model that can detect subevent relations in addition to full coreference. Second, we propose a method to improve subevent structure based on subevent clusters detected by the model. Using a corpus in the Intelligence Community domain, we show that the method achieves over 3.2 BLANC F1 gain in detecting subevent relations against the logistic regression model.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/963_Paper.pdf
Markdown (Informal)
[Detecting Subevent Structure for Event Coreference Resolution](http://www.lrec-conf.org/proceedings/lrec2014/pdf/963_Paper.pdf) (Araki et al., LREC 2014)
ACL