@inproceedings{hsu-etal-2024-argument,
title = "Argument-Aware Approach To Event Linking",
author = "Hsu, I-Hung and
Xue, Zihan and
Pochhi, Nilay and
Bansal, Sahil and
Natarajan, Prem and
Srinivasa, Jayanth and
Peng, Nanyun",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.758",
doi = "10.18653/v1/2024.findings-acl.758",
pages = "12769--12781",
abstract = "Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by examining their associated arguments. Moreover, the information-rich nature of events leads to the scarcity of event KBs. This emphasizes the need for event linking models to identify and classify event mentions not in the KB as {``}out-of-KB,{''} an area that has received limited attention. In this work, we tackle these challenges by introducing an argument-aware approach. First, we improve event linking models by augmenting input text with tagged event argument information, facilitating the recognition of key information about event mentions. Subsequently, to help the model handle {``}out-of-KB{''} scenarios, we synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments. Our experiment across two test datasets showed significant enhancements in both in-KB and out-of-KB scenarios, with a notable 22{\%} improvement in out-of-KB evaluations.",
}
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<abstract>Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by examining their associated arguments. Moreover, the information-rich nature of events leads to the scarcity of event KBs. This emphasizes the need for event linking models to identify and classify event mentions not in the KB as “out-of-KB,” an area that has received limited attention. In this work, we tackle these challenges by introducing an argument-aware approach. First, we improve event linking models by augmenting input text with tagged event argument information, facilitating the recognition of key information about event mentions. Subsequently, to help the model handle “out-of-KB” scenarios, we synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments. Our experiment across two test datasets showed significant enhancements in both in-KB and out-of-KB scenarios, with a notable 22% improvement in out-of-KB evaluations.</abstract>
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%0 Conference Proceedings
%T Argument-Aware Approach To Event Linking
%A Hsu, I-Hung
%A Xue, Zihan
%A Pochhi, Nilay
%A Bansal, Sahil
%A Natarajan, Prem
%A Srinivasa, Jayanth
%A Peng, Nanyun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F hsu-etal-2024-argument
%X Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by examining their associated arguments. Moreover, the information-rich nature of events leads to the scarcity of event KBs. This emphasizes the need for event linking models to identify and classify event mentions not in the KB as “out-of-KB,” an area that has received limited attention. In this work, we tackle these challenges by introducing an argument-aware approach. First, we improve event linking models by augmenting input text with tagged event argument information, facilitating the recognition of key information about event mentions. Subsequently, to help the model handle “out-of-KB” scenarios, we synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments. Our experiment across two test datasets showed significant enhancements in both in-KB and out-of-KB scenarios, with a notable 22% improvement in out-of-KB evaluations.
%R 10.18653/v1/2024.findings-acl.758
%U https://aclanthology.org/2024.findings-acl.758
%U https://doi.org/10.18653/v1/2024.findings-acl.758
%P 12769-12781
Markdown (Informal)
[Argument-Aware Approach To Event Linking](https://aclanthology.org/2024.findings-acl.758) (Hsu et al., Findings 2024)
ACL
- I-Hung Hsu, Zihan Xue, Nilay Pochhi, Sahil Bansal, Prem Natarajan, Jayanth Srinivasa, and Nanyun Peng. 2024. Argument-Aware Approach To Event Linking. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12769–12781, Bangkok, Thailand. Association for Computational Linguistics.