@inproceedings{yu-etal-2023-event,
title = "Event Linking: Grounding Event Mentions to {W}ikipedia",
author = "Yu, Xiaodong and
Yin, Wenpeng and
Gupta, Nitish and
Roth, Dan",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.196",
doi = "10.18653/v1/2023.eacl-main.196",
pages = "2679--2688",
abstract = "Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines the Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather the training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system, EVELINK, to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitations of the current model. Overall, as our analysis shows, Event Linking is a challenging and essential task requiring more effort from the community.",
}
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<abstract>Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines the Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather the training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system, EVELINK, to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitations of the current model. Overall, as our analysis shows, Event Linking is a challenging and essential task requiring more effort from the community.</abstract>
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%0 Conference Proceedings
%T Event Linking: Grounding Event Mentions to Wikipedia
%A Yu, Xiaodong
%A Yin, Wenpeng
%A Gupta, Nitish
%A Roth, Dan
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yu-etal-2023-event
%X Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines the Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather the training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system, EVELINK, to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitations of the current model. Overall, as our analysis shows, Event Linking is a challenging and essential task requiring more effort from the community.
%R 10.18653/v1/2023.eacl-main.196
%U https://aclanthology.org/2023.eacl-main.196
%U https://doi.org/10.18653/v1/2023.eacl-main.196
%P 2679-2688
Markdown (Informal)
[Event Linking: Grounding Event Mentions to Wikipedia](https://aclanthology.org/2023.eacl-main.196) (Yu et al., EACL 2023)
ACL
- Xiaodong Yu, Wenpeng Yin, Nitish Gupta, and Dan Roth. 2023. Event Linking: Grounding Event Mentions to Wikipedia. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2679–2688, Dubrovnik, Croatia. Association for Computational Linguistics.