@inproceedings{wenjia-etal-2023-improving,
title = "Improving Affective Event Classification with Multi-Perspective Knowledge Injection",
author = "Wenjia, Yi and
Yanyan, Zhao and
Jianhua, Yuan and
Weixiang, Zhao and
Bing, Qin",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.66/",
pages = "773--785",
language = "eng",
abstract = "{\textquotedblleft}In recent years, many researchers have recognized the importance of associating events withsentiments. Previous approaches focus on generalizing events and extracting sentimental in-formation from a large-scale corpus. However, since context is absent and sentiment is oftenimplicit in the event, these methods are limited in comprehending the semantics of the eventand capturing effective sentimental clues. In this work, we propose a novel Multi-perspectiveKnowledge-injected Interaction Network (MKIN) to fully understand the event and accuratelypredict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage con-texts to provide sufficient semantic information and perform context modeling to capture thesemantic relationships between events and contexts. Moreover, we also introduce human emo-tional feedback and sentiment-related concepts to provide explicit sentimental clues from theperspective of human emotional state and word meaning, filling the reasoning gap in the senti-ment prediction process. Experimental results on the gold standard dataset show that our modelachieves better performance over the baseline models.{\textquotedblright}"
}
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<abstract>“In recent years, many researchers have recognized the importance of associating events withsentiments. Previous approaches focus on generalizing events and extracting sentimental in-formation from a large-scale corpus. However, since context is absent and sentiment is oftenimplicit in the event, these methods are limited in comprehending the semantics of the eventand capturing effective sentimental clues. In this work, we propose a novel Multi-perspectiveKnowledge-injected Interaction Network (MKIN) to fully understand the event and accuratelypredict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage con-texts to provide sufficient semantic information and perform context modeling to capture thesemantic relationships between events and contexts. Moreover, we also introduce human emo-tional feedback and sentiment-related concepts to provide explicit sentimental clues from theperspective of human emotional state and word meaning, filling the reasoning gap in the senti-ment prediction process. Experimental results on the gold standard dataset show that our modelachieves better performance over the baseline models.”</abstract>
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%0 Conference Proceedings
%T Improving Affective Event Classification with Multi-Perspective Knowledge Injection
%A Wenjia, Yi
%A Yanyan, Zhao
%A Jianhua, Yuan
%A Weixiang, Zhao
%A Bing, Qin
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G eng
%F wenjia-etal-2023-improving
%X “In recent years, many researchers have recognized the importance of associating events withsentiments. Previous approaches focus on generalizing events and extracting sentimental in-formation from a large-scale corpus. However, since context is absent and sentiment is oftenimplicit in the event, these methods are limited in comprehending the semantics of the eventand capturing effective sentimental clues. In this work, we propose a novel Multi-perspectiveKnowledge-injected Interaction Network (MKIN) to fully understand the event and accuratelypredict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage con-texts to provide sufficient semantic information and perform context modeling to capture thesemantic relationships between events and contexts. Moreover, we also introduce human emo-tional feedback and sentiment-related concepts to provide explicit sentimental clues from theperspective of human emotional state and word meaning, filling the reasoning gap in the senti-ment prediction process. Experimental results on the gold standard dataset show that our modelachieves better performance over the baseline models.”
%U https://aclanthology.org/2023.ccl-1.66/
%P 773-785
Markdown (Informal)
[Improving Affective Event Classification with Multi-Perspective Knowledge Injection](https://aclanthology.org/2023.ccl-1.66/) (Wenjia et al., CCL 2023)
ACL