@inproceedings{you-etal-2022-eventgraph,
title = "{E}vent{G}raph: Event Extraction as Semantic Graph Parsing",
author = "You, Huiling and
Samuel, David and
Touileb, Samia and
{\O}vrelid, Lilja",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Zavarella, Vanni and
Y{\"o}r{\"u}k, Erdem},
booktitle = "Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.case-1.2",
doi = "10.18653/v1/2022.case-1.2",
pages = "7--15",
abstract = "Event extraction involves the detection and extraction of both the event triggers and the corresponding arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models will be released as open-source.",
}
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<abstract>Event extraction involves the detection and extraction of both the event triggers and the corresponding arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models will be released as open-source.</abstract>
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%0 Conference Proceedings
%T EventGraph: Event Extraction as Semantic Graph Parsing
%A You, Huiling
%A Samuel, David
%A Touileb, Samia
%A Øvrelid, Lilja
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Zavarella, Vanni
%Y Yörük, Erdem
%S Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F you-etal-2022-eventgraph
%X Event extraction involves the detection and extraction of both the event triggers and the corresponding arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models will be released as open-source.
%R 10.18653/v1/2022.case-1.2
%U https://aclanthology.org/2022.case-1.2
%U https://doi.org/10.18653/v1/2022.case-1.2
%P 7-15
Markdown (Informal)
[EventGraph: Event Extraction as Semantic Graph Parsing](https://aclanthology.org/2022.case-1.2) (You et al., CASE 2022)
ACL
- Huiling You, David Samuel, Samia Touileb, and Lilja Øvrelid. 2022. EventGraph: Event Extraction as Semantic Graph Parsing. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 7–15, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.