@inproceedings{dai-etal-2017-using,
title = "Using Context Events in Neural Network Models for Event Temporal Status Identification",
author = "Dai, Zeyu and
Yao, Wenlin and
Huang, Ruihong",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-2040",
pages = "234--239",
abstract = "Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts. Therefore, we extract dependency chains containing context events and use them as input in neural network models, which consistently outperform previous models using local context words as input. Visualization verifies that the dependency chain representation can effectively capture the context events which are closely related to the target event and play key roles in predicting event temporal status.",
}
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%0 Conference Proceedings
%T Using Context Events in Neural Network Models for Event Temporal Status Identification
%A Dai, Zeyu
%A Yao, Wenlin
%A Huang, Ruihong
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F dai-etal-2017-using
%X Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts. Therefore, we extract dependency chains containing context events and use them as input in neural network models, which consistently outperform previous models using local context words as input. Visualization verifies that the dependency chain representation can effectively capture the context events which are closely related to the target event and play key roles in predicting event temporal status.
%U https://aclanthology.org/I17-2040
%P 234-239
Markdown (Informal)
[Using Context Events in Neural Network Models for Event Temporal Status Identification](https://aclanthology.org/I17-2040) (Dai et al., IJCNLP 2017)
ACL