@inproceedings{pouran-ben-veyseh-etal-2022-mee,
title = "{MEE}: A Novel Multilingual Event Extraction Dataset",
author = "Pouran Ben Veyseh, Amir and
Ebrahimi, Javid and
Dernoncourt, Franck and
Nguyen, Thien",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.652",
doi = "10.18653/v1/2022.emnlp-main.652",
pages = "9603--9613",
abstract = "Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE. To foster future research in this direction, our dataset will be publicly available.",
}
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<abstract>Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE. To foster future research in this direction, our dataset will be publicly available.</abstract>
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%0 Conference Proceedings
%T MEE: A Novel Multilingual Event Extraction Dataset
%A Pouran Ben Veyseh, Amir
%A Ebrahimi, Javid
%A Dernoncourt, Franck
%A Nguyen, Thien
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F pouran-ben-veyseh-etal-2022-mee
%X Event Extraction (EE) is one of the fundamental tasks in Information Extraction (IE) that aims to recognize event mentions and their arguments (i.e., participants) from text. Due to its importance, extensive methods and resources have been developed for Event Extraction. However, one limitation of current research for EE involves the under-exploration for non-English languages in which the lack of high-quality multilingual EE datasets for model training and evaluation has been the main hindrance. To address this limitation, we propose a novel Multilingual Event Extraction dataset (MEE) that provides annotation for more than 50K event mentions in 8 typologically different languages. MEE comprehensively annotates data for entity mentions, event triggers and event arguments. We conduct extensive experiments on the proposed dataset to reveal challenges and opportunities for multilingual EE. To foster future research in this direction, our dataset will be publicly available.
%R 10.18653/v1/2022.emnlp-main.652
%U https://aclanthology.org/2022.emnlp-main.652
%U https://doi.org/10.18653/v1/2022.emnlp-main.652
%P 9603-9613
Markdown (Informal)
[MEE: A Novel Multilingual Event Extraction Dataset](https://aclanthology.org/2022.emnlp-main.652) (Pouran Ben Veyseh et al., EMNLP 2022)
ACL
- Amir Pouran Ben Veyseh, Javid Ebrahimi, Franck Dernoncourt, and Thien Nguyen. 2022. MEE: A Novel Multilingual Event Extraction Dataset. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9603–9613, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.