@inproceedings{hong-etal-2018-incorporating,
title = "Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition",
author = "Hong, Yu and
Xu, Yang and
Ruan, Huibin and
Zou, Bowei and
Yao, Jianmin and
Zhou, Guodong",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1015",
pages = "177--189",
abstract = "Event relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that there isn{'}t any reliable clue for use. We follow the traditional statistical approach in this paper, speculating the relation class of the target events based on the relation-class distributions on the similar events. There is minimal supervision used during the speculation process. In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events. We test our method on the ACE-R2 corpus and compared our model with the fully-supervised neural network models. Experimental results show that we achieve a comparable performance to CNN while slightly better than LSTM.",
}
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%0 Conference Proceedings
%T Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
%A Hong, Yu
%A Xu, Yang
%A Ruan, Huibin
%A Zou, Bowei
%A Yao, Jianmin
%A Zhou, Guodong
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F hong-etal-2018-incorporating
%X Event relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that there isn’t any reliable clue for use. We follow the traditional statistical approach in this paper, speculating the relation class of the target events based on the relation-class distributions on the similar events. There is minimal supervision used during the speculation process. In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events. We test our method on the ACE-R2 corpus and compared our model with the fully-supervised neural network models. Experimental results show that we achieve a comparable performance to CNN while slightly better than LSTM.
%U https://aclanthology.org/C18-1015
%P 177-189
Markdown (Informal)
[Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition](https://aclanthology.org/C18-1015) (Hong et al., COLING 2018)
ACL